inspection capabilities for enhanced ship safety · inspection capabilities for enhanced ship...

Inspection Capabilities for Enhanced Ship Safety

D8.5 (WP8): State-of-the-art on current /methodologies

/tools/ practices for ship structures and machinery

Responsible Partner: USG

Contributor(s): ALL

Dissemination Level

PU Public x

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the Commission Services)

CO Confidential, only for members of the consortium (including the Commission Services)

This document is produced by the INCASS Consortium. The INCASS project is funded by the European

Commission under the Seventh Framework Programme (FP7/2007-2013). Grant Agreement n°605200

D4.1 (WP4) – Document Title

This document is produced by the INCASS Consortium, funded by the European Commission (FP7/2007-2013).

Grant Agreement n° 605200.

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Document information table

Contract number: 605200

Project acronym: INCASS

Project Coordinator: University of Strathclyde Glasgow

Document Responsible Partner: University of Strathclyde Glasgow USG

Deliverable Type: Report

Document Title : State-of-the-art on current /methodologies /tools/ practices for ship

structures and machinery

Document ID: D8.5 Version: 3

Contractual Date of Delivery: Actual Date of Delivery:

Filename: D8.5 State-of-the-art

Status: Draft version

Authoring & Approval

Prepared by

Author Date Modified Page/Sections Version Comments

Iraklis Lazakis 24/03/2014 ALL V0 Creation of the

document

Kim Tanneberger 01/07/2014 ALL V1 Technical content

ALL 25/07/2014 ALL V2 Technical content

Atabak Taheri 05/08/2014 ALL V3 Technical content

Approved by

Name Role Partner Date

Document Manager Iraklis Lazakis Project Coordinator USG 05/08/2014

Document

Approval 05/08/2014




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Executive Summary

This report, represents all the related literature, projects and research avtivities relevant

to INCASS. It is devided into two major sections of Structural state of the art and

Machinery state of the art. Both sections include review of methodologies and tools

represented by Classification Societies and other research activities from the partners

including previous EU projects. Structural section additionally represent Finite Element

Methods (FEM), inspection and maintenance methodologies, and Life cycle

Management (LCM) tools. On the other side, machinery section contains miantenace

integration techniques, condition monitoring methods, and performance and condition

assessing tools such as Vibrational analysis, Themography and Lub Oil analysis. These

literature review will help INCASS Consortium to identify the gaps and optimise its

own methodology. This would create an exceptional background for both machinery

and structural maintenace and inspection methodology of this EU FP7 project.




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Table of Contents

1 INTRODUCTION ................................................................................................... 10

2 SHIP STRUCTURES STATE-OF-THE-ART ....................................................... 12

2.1 CLASSIFICATION SOCIETIES’ INPUT ............................................................... 12

2.2 RESEARCH PROJECTS .................................................................................... 13

2.3 OTHER RESEARCH ACTIVITIES ...................................................................... 19

2.3.1 Inspection and Maintenance ..................................................................................... 19

2.3.2 Inverse Finite Element Method (iFEM) ................................................................... 21

2.3.3 Lifecycle Data Management Tool Development ...................................................... 23

2.3.4 Lifecycle Data Interchange and Standards ............................................................... 24

2.3.5 Lifecycle Management Integration with Repairs ..................................................... 25

2.3.6 Lifecycle Management Integration with structural health monitoring systems ....... 27

2.3.7 Maintenance Methodologies .................................................................................... 28

2.3.8 Maintenance Analysis Tools .................................................................................... 30

3 SHIP MACHINERY STATE-OF-THE-ART ......................................................... 33

3.1 CLASSIFICATION SOCIETIES’ INPUT ............................................................... 33

3.1.1 Integration with Maintenance Management ............................................................. 34

3.1.2 The P – F Interval ..................................................................................................... 35

3.1.3 On-Line vs. Off-Line CM Systems ........................................................................... 36

3.1.3 Motivation with respect to Machinery Maintenance ................................................ 39

3.1.5 BV - Condition Monitoring ...................................................................................... 43

3.1.6 LR - Condition Monitoring ...................................................................................... 44

3.1.7 RINA - Condition Monitoring .................................................................................. 45

3.3 MACHINERY CONDITION MONITORING TOOLS ............................................... 47

3.3.1 Vibration monitoring ................................................................................................ 47




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3.3.1.1 Vibration Parameters ........................................................................ 48

3.3.1.2 Vibration Measurements .................................................................. 49

3.3.1.3 Standardization of the measurement ................................................ 49

3.3.1.4 Calibration ........................................................................................ 51

3.3.1.5 Baseline Measurements .................................................................... 51

3.3.2 Vibration Analysis - broadband vibration ................................................................ 52

3.3.3 Vibration Analysis - Vibration limits for electric motor driven rotating machinery . 52

3.3.4 Vibration Analysis - Frequency Spectrum Analysis ................................................. 53

3.3.5 Vibration Analysis - Minimum Technical Characteristics of the Measurement

Instrumentation ................................................................................................................... 55

3.3.6 Thermography ........................................................................................................... 55

3.3.7 Lubricating oil analysis ............................................................................................. 56

3.3.8 Monitoring of combustion parameters ...................................................................... 59

3.3.9 Partial discharge measurement techniques ................................................................ 59

3.3.10 Current analysis techniques ................................................................................. 60

3.3.11 Monitoring architecture topologies ..................................................................... 60

3.3.12 OTHER RELATED METHODOLOGIES ............................................................... 62

3.4 STATE-OF-THE-ART ON CONDITION BASED MAINTENANCE (CBM) .............. 63

3.4.1 Theory Underlying the Determination of CBM Task Intervals ................................. 65

3.5 OTHER CONDITION MONITORING AND MACHINERY RELATED STATE

LITERATURE ............................................................................................................. 67

4 CONCLUSION ....................................................................................................... 70

5 REFERENCES ........................................................................................................ 71




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Table of Figures

Figure 1 - Failure probability in relation to operation time ............................................ 20

Figure 2 - Typical plot of vibration readings .................................................................. 53

Figure 3 - Typical vibration signature ............................................................................ 54

Figure 4 - Mass of Metal Particle Detection in Oil ........................................................ 58

Figure 5 - Ferromagnetic vs. Non-Ferromagnetic .......................................................... 58

Figure 6 - Diagram of the monitoring system ................................................................ 62

Figure 7 - CBM in different industrial sectors ............................................................... 65




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Table of Tables

Table 1 - Vibration Limits for Electric Driven Rotating Machinery .............................. 52

Table 2 - Temperature to Maintenace Scheduling Relation Table ................................. 56

Table 3 - Parameter Condition Detection Table ............................................................. 57




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Nomenclature

Acronym Meaning

FEM Finite Element Methods

iFEM Invese Finite Element Methods

LCM Lif e Cycle Management

AMS Advisory Monitoring System

LBSG Long Base Strain Gages

ROV Remotely Operated Vehicles

UAV Unmanned Ariel Vehicle

FPSO Floating Production, Storage and Offloading

ROT Robots in Tanks

HUAV Hovering Autonomous Underwater Vehicle

LHS Latin Hypercube Sampling

SHM Structural Health Monitoring

RDIF Radio-Frequency Identification Device

RCBM Reliability and Criticality Based Maintenance

DSV Diving Support Vessel

DFTA Dynamic Fault Tree Analysis

F-V Fussell-Vesely

BN Bayesian network

LNG Liquefied Natural Gas

FSRU Floating Storage and Regasification Unit

LCA life cycle assessment

LCC life cycle costing

DMU Visualisation & Digital Mock-up

RBI Risk-Based Inspection

MDP Markov Decision Process

VDM Value Driven Maintenance

RRCM Reliability and Risk Cantered Maintenance

TPM Total Productive maintenance

ERP Enterprise Recourse Planning

CSS Critical Success Strategies

HCA Hull Condition Assessment

FMECA Failure Mode, Effect and Criticality Analysis

RCM Reliability Centred Maintenace

CTQ Critical To Quality

HAZID HAZard Identification

HAZOP HAZard and OPerability

MCDM Multi Criteria Decision Making

FTA Fault Tree Analysis




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BBN Bayesian Belief Network

AHP Analytic Hierarchy Process

MCDA Multi Criteria Decision Analysis

NSC New Service Concept

SWOT

Strengths, Weaknesses, Opportunities and

Threats

CMMS

Computerized Maintenace Manangement

System

CBM Condition Based Maintenace

PDC Portable Data Collector

CM Condition Monitoring

MCM Machinery Condition Monitoring

MPMS Machinery Planned Maintenance Scheme

SPM Shock Pulse Analysis Method

IPMS Integrated Platform Management System

RBI Risk Based Inspection

FPU Floating Production Unit

DSS Decision Support Systems

ILM Ice Load Monitoring

QFD quality Function Deployment

ICT Information and Communication Technologies

COP Coefficient Of Performance

EA Evolutionary Algorithm

GTM Generative Topographic Mapping

BIP Bayesian-Inference-based Probability

BOCR Benefits, Opportunities, Costs and Risks




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1 Introduction

Ship accidents and near misses can be frequently attributed to the failure of structures

and machinery. Accordingly, the latter increases the risk for crew and passengers’

injuries and fatalities, environmental damage and pollution, damage or total loss of the

ship and its equipment as well as disruption of the ship’s operations which consequently

lead to operational losses. Additionally, the maritime regulatory and administration

authorities such as Flag states, Port State Control authorities and Classification Societies

have increased their cooperative efforts towards the promotion of safe, secure and

environmental friendly ship operations over the last years. The latter has occurred

through both formal cooperation among countries (e.g. Paris Memorandum Of

Understanding-MOU, etc.) as well as the form of guidelines introduced by other

maritime stakeholders (e.g. OCIMF, IACS). In all cases, all relevant bodies attempt to

preserve the highest standards in the maritime industry while at the same time make

every effort in order to minimise the high-risk and sub-standard ships.

In an effort to address the rules and Standards for Ship Inspection and Survey

Organisations, EC published regulation No 391/2009 of the European Parliament and of

the Council with particular focus on the standardised and harmonised framework related

to ship inspections and surveying (EC 2009). In this document, key areas of interest

mentioned are:

‘the harmonisation of the rules for the design, construction and periodic survey

of merchant ships’

‘public right of access to information’

‘access to ships and ship files regardless of the ship’s flag’

‘…development and implementation of safety requirements for hull, machinery

and electrical and control installations of ships falling under the scope of the

international conventions’




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On top of the above, ship managers/operators still try to find a way to combine the rich

practical knowledge acquired in the actual marine field with the technological advances

stemming from the relevant information technology sector in an effective way. The

latter comes in addition to the effects of not applying the appropriate maintenance

sequence onboard a ship. Moreover, when repair works and/or spare parts are needed

onboard the vessel, they have to be planned well in advance as the ship sails in different

geographical locations, thus with significant functional/access restrictions.

Besides the above, the overall risk analysis, risk management and maintenance process

in the maritime sector still lacks the element of applying and implementing

technologically advanced tools in contrast to applications in other industrial sectors such

as the nuclear and aerospace industry which provide real-time monitoring (e.g.

condition monitoring tools and techniques). In this case, condition based procedures in

the maritime industry are not well established yet (Imarest 2011).

In brief, this report will represent state of the art for structures at section 2, which

contains relevant previous research activities, methodologies and tools used in industry.

This also includes class inputs on structural maintenance, Finite Element Methods

(FEM) and Lif e Cycle Management (LCM) methodologies. Subsequently, section 3

will discuss related machinery state of the art for INCASS including condition

monitoring tools (e.g. Vibrational analysis, Thromography and Lub Oil analysis) and

methodologies. Finally, it will conlude with overall preview of the major points

obtained from state of the art.




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2 SHIP STRUCTURES STATE-OF-THE-ART

This section will provide background information on state of the art relevant to

structural inspection and maintenance section of the INCASS. It wil introduce previous

knowledge inputs from class societies, previouse EU research projects, other research

activities on Inspection & Maintenace, Inverse Finite Element Method (iFEM), Life

Cycle Management Systems, Maintenace Methodologies and Tools.

2.1 Classification Societies’ Input

There few off-the shelf structural monitoring systems are developed. One of them is

called OCTOPUS-MONITAS, which is third generation Advisory Monitoring System

(AMS) software. This tool can provide gridlines on abnormalities observed on the

actual fatigue consumption from design predictions. This can then be and interpreted

into the monitoring data for operational supervision and feedback to the designers

(Aalberts, 2011). Another tool is called MON-Hull which is a hull stress and motion

monitoring system. This system can supply real-time data update on hull girder

longitudinal stresses and vertical accelerations on both sailing and loading-offloading

situations. It also optimises data into sets of only crucial statistical results, which is

periodically updated, displayed and stored. Further information can be also

supplemented by the Owner as an addition to its logbook.

There are comprehensive research has been carried out by classification societies with

other industrial partners on hull structural monitoring systems. One of the major

projects on this matter is carried out by BV, where they have developed a fully

operational and installed onboard structural monitoring system on one of the latest

ULCS (Baudin, Bigot, Derbanne, Sireta, & Quinton, 2013).. On this system, the

structural excitation is evaluated in conjunction with sea state measurements system.




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Additionally, numerous structural monitoring sensors (twelve three-axis accelerometers

and eighteen Long Base Strain Gages (LBSG)) are installed to illustrate the general

structural response. On another research, ice loads and its properties has been measured

on polar conditions in order to define their effects on vibrations on shaft and hull of the

vessel. This study also discovered that the general ship manoeuvres can extensively

increase the stress levels on both shaft and the hull (Bekker, et al., 2014).

2.2 Research Projects

This need has been identified in the maritime transport sector and has introduced two

EC funded research paradigms, namely the HCA-Flagship (Emmett et al, 2011) effort

and RISPECT project (Barltrop. et al, 2010), which offer decision support tools to assist

in the decision making process, prediction for possible areas of defect depending on

parameters like the ship’s type, age and size, with knowledge/information collected

from past experiences (databases) and updated with current survey data.

Online hull stress monitoring systems have been proposed during the previous years, for

example by the OPTINAV project (OPTINAV 2012), where hull stress fatigue cycles

were recorded by a sensor network of strain gauges and accelerometers in order to

compare them against the acceptable limits and assist in the navigation of the ship (like

selection of route depending on the sea-states and weather conditions and their

projected effects on the vessel’s hull). Within the objectives of OPTINAV, integration

of the collected data was foreseen in order to provide a comprehensive database for

future reference and use by similar systems (the ones developed under Flagship-HCA &

RISPECT for example).

The RISPECT (Risk-Based Expert System for Through-Life Ship Structural Inspection

and Maintenance and New-Build Ship Structural Design) project combined the

traditional experience-based inspection method with the first-principles, statistical

analysis, for safe, cost-effectiveness structural inspection, repair and design rule




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improvement of existing ships (RISPECT, 2012). It provided aids to the structural

integrity management of ships that can be succeeded by optimizing the overall

management of shipping operations. More specifically, RISPECT developed tools for

assessing structural loads, stresses and strength. Additionally, a standard database

structure was also created in order to be accessed by Classification Societies and ship

operators. This was populated with sanitised data from survey results in order to avoid

duplications and sensitive information being released, while at the same time

maintaining the significance of the collected information. RISPECT was oriented on

tanker ships in order to challenge the creation of a methodology for performing

inspections on-board, recording tools and codification for minimization of risk of error

during the inspection data collection. Currently, the RISPECT system consists of the

following structural modules that are connected to each other:

Hydro-Static/Dynamic Pressure Calculations

Global and Member Force Calculations

Extreme and Fatigue Global & Member Force Calculations

Local Structure & Crack Calculations

Coating Breakdown Anode Loss & Corrosion Analysis

Structural Strength & Reliability Calculations

Strength & Fatigue Check

However, no real-time information was collected while the integration of the influence

of the hydrodynamic performance of the ship was not considered. Moreover, the

analysis performed was not centred on risk-based approaches, thus identifying the

critical ship structural areas and consequently ships. This is an area at which the

INCASS project will aim for. In this respect, the scope of the MINOAS (Marine

Inspection rObotic Assistant System) project comprised the inspection of both dry and




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wet areas of the vessel (e.g. flooded ballast tanks or external hull) (Bibuli et al, 2011).

Moreover, the MINOAS project was not limited to tele-operated floating tethered

vehicles and considered a varied set of robotic technologies with different locomotion

capabilities and different degrees of autonomy. Conveniently used in sequence or in

cooperation they all intended to “teleport” the surveyor to the different points of interest

of the vessel hull during inspection. Visual data and thickness measurements were

wirelessly transmitted to the respective control station.

In more detail, the MINOAS concept comprised aerial vehicles, magnetic crawlers, and

ROVs. The aerial vehicle, due to its mobility, was intended to provide a fast overview

of the state of the structure under inspection, e.g. a cargo hold of a ship. Fitted with a

flexible set of cameras that could be oriented towards the directions of interest, it was

able to self-locate within the environment and provide visual information tagged with

the position where the vehicle was when the picture was taken. A lightweight magnetic

crawler was next intended to be used for providing richer imagery from the hull points

of interest identified by the UAV. In case thickness measurements were needed, a

second magnetic crawler fitted with an arm and an end-effector with all the necessary

for ultrasound thickness (UT) measuring (i.e. grinding, cleaning, and probe sampling)

could be deployed at the related hull points. An ROV equipped with a UT measuring

tool was the last element of the fleet, intended for visual inspection and thickness

measurement within submerged vessel areas.

In addition to the above, past EU-funded projects like ROTIS - Remotely Operated

Tanker Inspection System (Meo, G. and Papalia, B., 2001) and its follow-up ROTIS-II

(Prendin, 2004) represent remarkable initiatives oriented towards introducing robotic

technologies within the overall inspection and monitoring strategy. ROTIS aimed at

developing a small vehicle designed to perform inspections of ship's ballast tanks, being

able to operate on oil tankers, on dry- and mixed cargo carriers and on FPSO (Floating

Production, Storage and Offloading) units. During operation, the vehicle was introduced

within flooded ballast tanks, between the inner and the outer hull, with access to




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virtually all cells and structural parts of a double hull vessel through standard man-holes

and openings.

CView project referred to the development, analysis and evaluation of semi-

autonomous inspection for underwater structures and ship hulls (Kirchner, 2009).

Sensors acquired data by camera systems, laser projection and multi-beam echo

sounder. The major goal of this project was the development of a semi-autonomous

inspection that could be mounted on underwater vehicles and provide the ability to

detect malfunctions at underwater structures and ship hulls as well. Additionally, the

ROT (Robots in Tanks) project aimed at setting the foundation for applications in

inspection and maintenance of complex-shape and difficult access tanks such as ballast

water tanks (BWTs) on vessels. This project introduced new methods for mobile and

partly autonomous robots that were needed to operate under extreme conditions. A

combination of existing state of the art and new designs of control and system

architectures, efficient communication technologies for closed spaces, as well as the

necessary sensors and actuators were taken place. Similarly to the ROT project,

RoboShip (Robotic Ship) proposes the investigation and inspection of ballast water

tanks (Bongerink, 2012).

The MARSTRUCT (Marine Structures) project was supported by the Sixth Framework

Programme (FP6) focusing on Marine Structures. MARSTRUCT aimed at improving

the effectiveness, safety, reliability and environmental performance of ship structures

(Pina, 2005). The project was oriented on the development of advanced structural and

reliability assessment within design, fabrication and operation. The work undertaken as

part of this project was related to methods and tools for loads, load effects, strength

assessment, experimental analysis of structures, methods and tools for structural design

and optimization and structural reliability, safety and environmental protection among

others.

Additionally, the ALERT (Assessment of Life-cycle Effect of Repairs on Tankers’)

project examined the cumulative effect of repairing a tanker throughout its life looking




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for best industry practices and ways in which these practices could be improved

(Downes et al 2008). ALERT focussed on the importance and need of using

computerised management information systems (MIS). It also examined the importance

of coating on ship structures as well as electromechanical techniques measuring

degradation and corrosion.

On the other hand, IMPROVE (Design of Innovative Ship Concepts using an Integrated

Decision Support System for ship Production and Operation) was an FP6 EU funded

project which suggested the integration of a decision support system with the

methodological assessment of new ship designs (Rigo, 2009). As part of the overall

project aims, the reliability characteristics of the three ship types were explored in order

to minimise production and maintenance costs and consequently increase their

operational availability profile.

The BESST (Breakthrough in European Ship and Shipbuilding Technologies) project

scope was to improve extensively in the domain of competitiveness; environmentally

friendliness and safety of European build ships (Roland, 2009). An extensive life cycle

performance assessment on ship level supported the technical developments on system

level. Furthermore, among the project’s technical innovations were the space

optimisation and easy maintenance, improved reliability through condition monitoring

and optimization of logistic chains. The findings of all these innovative projects will be

utilised directly in some cases and have the research expanded upon on others in order

to support the INCASS project’s objectives on harmonised and improved inspection

capabilities in order to enhance safety and environmental protection.

The need for hull stress monitoring systems has been recognized by the market leading

to the commercial production of such systems like the ones by SST - Sea Structure

Technology (SST, 2012) or StressAlert (StressAlert, 2006), leading to a point where

such systems are commonly met as add-on equipment – yet still not required - in new-

buildings. The regulatory regime has tracked this trend by updating the current

legislation in order to include guidelines and certification of such systems to the




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existing rule-set, for example (DNV 2003) and the set of HMON rules (DNV 2012).

Yet, such hull stress monitoring systems usually require the set-up of a sensor network

as a permanent installation onboard the ship, which is often the case in new-buildings,

leaving a large portion of the fleet unaccounted for.

The need for hull stress monitoring systems has been recognized by the market leading

to the commercial production of such systems like the ones by SST - Sea Structure

Technology (SST, 2012) or StressAlert (StressAlert, 2006), leading to a point where

such systems are commonly met as add-on equipment – yet still not required - in new-

buildings. The regulatory regime has tracked this trend by updating the current

legislation in order to include guidelines and certification of such systems to the

existing rule-set, for example (DNV 2003) and the set of HMON rules (DNV 2012).

Yet, such hull stress monitoring systems usually require the set-up of a sensor network

as a permanent installation onboard the ship, which is often the case in new-buildings,

leaving a large portion of the fleet unaccounted for.




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2.3 Other Research Activities

2.3.1 Inspection and Maintenance

In general, designing ships too strong makes them heavy, slow and very costly to build

and operate since their cargo space is decreased. In contrast, structural failures, hull

damage, weather conditions can easily cause a big injury or in extreme cases a

catastrophic failure and sinking of ships which are designed weak. Therefore, the

structural strength of ships is a key topic that affects safety of crew, economic cost, and

the pollution of the environment in which ships are trading. The required structural

management and safety of ship can be achieved by performing appropriate inspections

at the right intervals and repairing defects that are identified.

The importance of using autonomous underwater vehicles has recently increased in

inspection of ship hulls and marine structures after more challenging application of

robotics emerged. Hover et. al. (2012) constructed and applied navigation algorithms

that can control the hovering autonomous underwater vehicle (HUAV) in order to

achieve full imaging coverage of hull structure at high resolution, better simultaneous

localization, and mapping process. According to the experiment that was conducted in

Hover et. al. (2012), it has been proven that HUAV can operate effectively on all parts

of a vessel and produce necessary images and mesh model.

Today’s structural reliability applications have a great impact on determination of

inspection and maintenance planning. The assessment of structural strength by using

probabilistic techniques is one of the most popular structural reliability applications.

The target of this method is to calculate failure probability of the system by evaluating

the reliability index for the assumed scenarios. Camara and Cyrino (2012) developed a

statistical model of hull structure containing the effects of fatigue and corrosion. Based

on the developed model, time-dependent reliability of the hull structure was calculated




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by using Monte Carlo Simulation. Figure 1 illustrates the probability of failure change

versus operation time for considered model in Camara and Cyrino (2012). It was

concluded that maintenance/inspection time, which should be done around 7.5 years of

operation for the considered model, can be arranged by using target reliability.

Figure 1 - Failure probability in relation to operation time

A fast integration technique based on the first order reliability methods was adopted by

Zayed et. al. Zayed et. Al. (2013a) and Zayed et. Al, (2013b) to calculate the structural

reliability of ship hulls effectively. It was initiated that the ultimate vertical bending

moment capacity is selected as a limit state of hull structure. As a result, the first order

methods were observed to be theoretically straightforward, requires less numerical

effort and computationally efficient. Guo et. al. (2012) used Latin Hypercube Sampling

(LHS) method with Monte Carlo Simulation in order to determine the failure

probability level for corroded aging tankers.

Probability distribution of each variable was divided into non-overlapping segments and

a value for each variable can randomly be generated from each segment in LHS

procedure so that the total variety of the distribution is sampled more evenly and

steadily. This study concluded that LHS is mainly more accurate than conventional

Monte Carlo direct sampling during the simulation. Structural Health Monitoring




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(SHM) data obtained from sensors was used by Zhu and Frangopol (2013) to improve

the accuracy and redundancy of reliability assessment of the ship cross-sections. Prior

load effects were updated according to SHM data related to the wave-induced load by

using Bayesian updating method. This study concluded that integration of the SHM data

can considerably decrease the uncertainty in a distribution parameter, and hence

updated performance indicators come closer to correct values. Nowadays, inspection is

still generally done by using papers and pens, although it requires a lot of time to

transfer the inspection data from hand written documents to computer system.

Li et. al. (2012) introduced a hull structure information integration model that can be

used in mobile devices in order to record the data during the inspection process, and

therefore transferring this data is much easier than conventional ways. In this study, it

was proposed that the large amount of the problems related to inspection of marine

structures can be efficiently solved by using this mobile application. Additionally,

maintenance operations must be done based on accurate information provided via

inspection. Lee et. al. (2013) suggested a radio-frequency identification device (RDIF),

which can be applied to cloud technology, for an effective maintenance/inspection

operation. It was proposed that RDIF can be used to identify correct data and save this

data accurately via a cloud system, thus if RDIF is used during inspection process, it

will improve the efficiency of maintenance operations.

2.3.2 Inverse Finite Element Method (iFEM)

Real-time reconstruction of full-field structural displacements is the key component of

structural health monitoring by utilizing the strain data obtained from sensors at various

locations of a structure. To enable such abilities, load-carrying structural components

can be instrumented with a linkage of strain sensors, e.g., fiber optic strain system

described in Froggatt and Moore (1998). Reconstruction of a displacement vector at

every material point of the structure from a set of discrete strain measurements

establishes an inverse mathematical problem. An algorithm that is robust, stable,




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accurate, fast and general enough to take into account complex structural topologies was

recently introduced and named as Inverse Finite Element Method by Tessler and

Spangler (2005).

iFEM formulation uses a least-squares variational principle and the mathematical

foundation is explicitly described. The formulation involves the entire structural

geometry that is discretized by using suitable inverse finite elements in which the

measured strain data are adapted to the element strains in a least-square sense. As a

consequence, a system of linear algebraic equations needs to be solved to determine the

unknown displacements which leads one to find the deformed structural shape at any

real-time. Static and dynamic behaviour can be obtained without prior knowledge of

material properties and loading. Vazquez et al. (2005) examined the capability of a

structural health monitoring that uses distributed fiber optic system and iFEM at NASA

Langley Research Centre. They indicated that the practical implementation of iFEM on

a structure is computationally ultra-fast for a real-time application without sacrificing

accuracy.

iFEM is applicable to thin and moderately thick beam, plate and shell structures.

Timoshenko beam theory was adopted, including stretching, bending, transverse shear

and torsion deformation modes by Gherlone et al. (2014), in order to demonstrate that

iFEM for beam and frame structures is reliable when experimentally measured strain

data is used as input (Gherlone et. al. (2012)). Shkarayev et al. (2001), and Tessler and

Spangler (2003), focused on the inverse problem of reconstructing the three-

dimensional displacements in plate and shell structures from strain sensor

measurements. A three-node, inverse-shell element, iMIN3, was developed having six

degrees of freedom at each node, i.e., three displacements and three rotations (Tessler

and Sprangler (2004)).

The kinematic variables were interpolated using linear in-plane displacements and

bending rotations, and a constrained type quadratic deflection. A computational

example was presented for a statically loaded cantilevered plate for which




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experimentally measured strains had been obtained in a structures laboratory.

Application of iFEM was demonstrated on this problem and comparisons with the

measured deflections and those obtained using the direct FEM was discussed in.

2.3.3 Lifecycle Data Management Tool Development

An advanced smart maintenance system has been developed by Lee et al. (2012a) that

can provide onsite engineering data to field engineers including 3D CAD design

information in working process. RFID technology is applied to derive exact information

into the smart maintenance system where it has been incorporated into mobile devices.

Subsequently, A framework is presented by Dylan and Matthew (2013) to schedule

maintenance cycles for naval vessels minimizing the lifetime costs of the structure of a

notational DTMB-5145. Another maintenance strategy was introduced by Lazakis et al.

(2012a) called Reliability and Criticality Based Maintenance (RCBM). This

methodology was applied for creating optimum maintenance system onboard a Diving

Support Vessel (DSV). Reliability and criticality analysis of the main systems of the

vessel are the starting point of this approach. Propulsion, Lifting, Anchoring & Hauling

and Diving systems are the subsystems analysed in this case study. Furthermore, by

using the Dynamic Fault Tree Analysis (DFTA) tool and the Birnbaum (Bir), Criticality

(Cri) and Fussell-Vesely (F-V) reliability importance measures, the results of the above

analysis have been validated

Bayesian network (BN) another similar tool like Fult Tree Analysis (FTA). Schleder et

al. (2012) have presented an application of BN to analyse different event scenarios

using the parent marginal probability distribution of each component. This requires

computation of the posterior joint probability distribution of component subsets and

function of the set of all nodes. This model has also been implemented on a Liquefied

Natural Gas (LNG) Regasification System on a Floating Storage and Regasification

Unit (FSRU). Gazis (2012) have looked into another type of probabilistic response

analysis and reliability assessment on subsea free spanning pipeline systems. These




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systems are exposed to random wave-induced hydrodynamic forces. Monte Carlo

simulation methodology is the chosen tool for the analysis. This study has demonstrated

the advantages of integrating a reliability-based design.

A non-stationary MDP system developed by (Niese and Singer, 2013) considers ballast

water exchange and treatment policy changes. Therefore, MDP to model life cycle

decisions on this research determines the summary of the initial approach, outcomes,

and conclusions resulting from the implementation of the mentioned framework.

Implementation of life cycle assessment (LCA) and life cycle costing (LCC) in marine

systems design is proven. A holistic approach has been developed by Fet et al. (2013)

that compares existing environmental assessment tools and introduces systems

engineering as a holistic approach to life cycle designs. ABB has also prepared an

insight into a holistic performance management and optimization of any vessel types,

that recognises energy efficiency, availability and safety of the vessel (Ignatius et al.,

2013). More specialized structural and data analysis tools are also developed in industry

such as simplified fatigue assessment rooted in beam theory with a spectral-based

fatigue analysis procedure in MAESTRO introduced by Hunter et al. (2013).

2.3.4 Lifecycle Data Interchange and Standards

In recent decade relays, push buttons and light-bulbs has been replaced by processors,

graphical user interfaces, keyboards and track balls. Therefore, high level computer

languages like C++ and JAVA are becoming a norm in marine industry. Now it is

possible to maintain electronic copies of vessels’ history throughout its service life in a

centralized electronic location. Thus, Scherer and Cohen (2011) have discussed about

the Naval Surface Warfare Center, Ship Systems Engineering Station (NAVSSES) as a

centre for collecting machinery system data and its management. Siemens Industry have

also created software using light-weight 3D neutral format JT for shipbuilding (Malay,

2012). This uses Visualisation & Digital Mock-up (DMU) , Documentation &

Archiving , and Data Exchange. JT format can facilitate data exchange between all




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stakeholders in a shipbuilding project. This can be implemented for Product Lifecycle

Management providing a shared platform for efficiently saving, representing, and

organising, recovering and recycling product-related lifecycle information.

A paper from Goni and Jambrina (2013) have discussed about CAD design language

implemented on ship design concept using three-level software architecture. The

development framework has been considered in Windows 8 using web applications

such as HTML5, CSS3, JavaScript, and for other native applications using C/C++. NET

applications can be also developed using C#, VB and F#. The user interface and user

experience for non-web application is described with XAML and the 3D API for games

and design applications is DirectX.

Standards are important on every stage of vessel’s life cycle. That is why Shin et al.

(2012) have developed a prototype of ship basic planning system for the small and

medium sized shipyards based on the advanced IT systems. For this analysis,

standardized development environment and tools are selected. These tools are used for

the system development for increasing competitiveness of small and medium sized

shipyards in the 21st century industrial environment.Thomson and Renard (2013) have

expressed the importance of ship design standards on their research. They have looked

into the 3D models of the as-built assets incorporating advanced information

management technologies. This paper also delivers a inclusive indication of challenges,

solutions and most suitable practice in the handover from shipbuilder to operator of a

complete digital information asset.

2.3.5 Lifecycle Management Integration with Repairs

One of the most important areas on repair integration with maintenance is the inventory

planning and organisation. A paper from Lutjen and Karimi (2012) represents an

optimized inventory system simulation approach for a single-echelon used in offshore

wind turbine installations. It performs a heuristic reactive scheduling for synchronizing




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the installation vessels on different weather conditions in supporting the planning of

offshore logistics systems. For logistics in marine environment, it is important to have a

decent data gathering of the environmental conditions and degradation processes.

Reliability and risk analysis are important tools that have to be used for inspection and

repair planning in general. The EU-FP7 Project – RISPECT, has generated an enhanced

Reliability/Risk-Based Methods for merging detailed analysis of large ship database to

define beneficial Risk-Based Inspection plans (Hifi et al., 2012). Consequently,

improved inspection strategies would result in analysis of more important defects for

raising the structural safety and lowering pollution. In another paper from Hifi and

Barltrop (2013) they have used the central database for forecasting structural defects.

This also helped them to create a reliability model that can consider individual

components. However, this would provide an insufficient representation of the overall

reliability of the ship. Therefore, they have produced a method to adjust the reliability

models using the data from experience-based methods.

Thus, the critical structural details have been used by the inspection companies, class

surveyors, ship managers and ship designers for the calibration of the inspection

planning for the decision support tool. This opens a discussion for the use of decision

making tools for repair planning which is considered in a paper by Nathan D. Niese

(2013). He has looked into optimal maintenance strategy for time-dependent

environmental agreement to be governed implementing a chronological decision-

making framework known as a Markov Decision Process (MDP) for ballast water

exchange and treatment policy changes.




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2.3.6 Lifecycle Management Integration with structural

health monitoring systems

First innovative study that can integrate structural health monitoring with maintenance

is an study by Ferrese et al. (2011) where a particle swarm optimization algorithm is

applied for obtaining an ideal control for a desired eigenstructure. Nonlinear power

system model has been used for this study where the algorithm is established to be

highly effective in the maintenance of the system output to the specified eigenstructure.

This proves the importance of structural integrity on planning. As a result, Caldwell

(2012) has illustrated hull integrity management of the Floating Offshore Installation

(FOI) and techniques used to monitor this structural aspect as class requirements for

hull inspection is five yearly survey cycles. One of the major methods of hull inspection

for vessels is dry-docking but FPSOs cannot be dry-docked. Therefore, structural

integrity management has been chosen to develop innovative techniques to support risk

based inspection of the FPSOs. In another study by Kvarme et al. (2012) structural

integrity assessment has be implemented for investigating the integrity of pipelines

based on information from external investigations.

Corrosion is the most vital phenomenon that has been taken into account on structural

analysis in marine environment. Thus, Htun et al. (2013) have looked into random field

model for demonstration of corroded surfaces. The surface geometry of corroded plate

has been considered using an innovative random field model called Kerhunen-Loeve

Expansion Method. This is an alternative methodology to more common uniform

corrosion models. Another work by Ostuni et al. (2013) have looked into the application

of Decision Support Systems (DSSs) on structural analysis management and their

application on shipboard security for supporting crew members in the effective conduct

on failure events. This is a knowledge-based DSS integrated within a Damage Control

System (DCS) for navies.




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Lifecycle analysis is another criteria on ship structural monitoring and management

which as evaluated on a study by Ohba et al. (2013). This study considers sustainability

assessment vessel structure on risk of accidents based on environmental, economical

and societal factors. As a result, Lifecycle structural optimization of the mid-ship

section of a double hull tanker has been carried out in this work. Five optimization

problems are evaluated in this are Minimization of construction cost, Optimisation of

Life Cycle Benefit (LCB) based on the oil outflow hazard, Maximization of LCB

considering the CO2 emissions hazard, Expansion of LCB considering the risk of

failure, and Optimisation of LCB considering all of the risk factors called the holistic

risk.

2.3.7 Maintenance Methodologies

CBM strategy implemented in manufacturing industry can use one of the three

approaches of Time-domain, Frequency domain and Time-Frequency domain (Bleakie

& Djudjanovic, 2013). Srinivasan & Parlikad (2013) have discussed the benefits of

using condition monitoring and CBM in maintenance of civil structures. Another

Maintenance methodology used in industry is called Reliability Centered Maintenance

(RCM). Hifi & Barltrop (2012) have developed an idea of combining RCM with

Condition monitoring for the maintenance and inspection of ship structures. They also

create a Central Statistical Database where subscribers can safely put their sensitive

data. Liu, et al. (2013) have used X control chart on in conjunction with CBM

methodology. In another paper by Hifi & Barltrop (2012), they have developed an idea

of combining RCM with Condition monitoring for the maintenance and inspection of

ship structures. They also create a Central Statistical Database where subscribers can

safely put their sensitive data.

There are numerous alterations of these maintenance methodologies are available in

research. Value Driven Maintenance (VDM) is another type of RCM methodology that

uses Performance goal-setting and measurement for the plant management. Main




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principle of VDM methodology is called Experience Based Reliability Centred

Maintenance (EBRCM). EBRCM is integration feedback data, decision logic, fault

modes, effects and criticality analysis (Rosqvist, Laasko, & Reunanen, 2009). Selvik &

Aven (2011) presented an updated version of RCM called Reliability and Risk Cantered

Maintenance (RRCM), which decreases the uncertainties. Turan, et al. (2011) have

created an innovative new RCM technique based on criticality analysis called

Reliability and criticality Based Maintenance (RCBM). Lazakis (2011) has added Total

Productive Maintenance (TPM) managerial aspects to the previous RCBM technique.

Enterprise Recourse Planning (ERP) is a maintenance management methodology that

has been used for management of all operational activities. Hoch & Dulebohn (2013) on

their paper has focused on the Human resource management aspect of the ERP, whereas

Huin, et al. (2003) have developed a Multi-flow Small and Medium sized Enterprise

(M_SME) system using the combination of artificial intelligence and ERP. However,

Aslan, et al. (2012) have questioned the implementation of off-the-shelf ERP systems.

Yeh & Xu (2013) have created a Critical Success Strategies (CSSs) system as

supplement for the ERP.

Hull Condition Assessment (HCA) is a critical activity in the maritime industry as it is

straightforwardly linked to the ship’s seaworthiness and the safety preconditions

mandated for the ship herself and the onboard personnel. On time identifications of

defects has also a significant monetary value for the ship operators as it allows for better

scheduling of maintenance activities and prohibits failure propagation effects, thus

minimizing the risks. Hull Condition Assessment is currently performed under two

directions:

the periodic Classification surveys, during which the hull status is compared to

some predefined nominal values (with metrics like hull and structural members

thicknesses or extent of rust or pitting) and




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During Condition surveys which are performed under the ship

owner’s/operator’s responsibility and usually have as objective the scheduling of

the repair activities.

2.3.8 Maintenance Analysis Tools

Failure Mode, Effect and Criticality Analysis (FMECA) is one of the most commonly

used maintenance analysis tools, where on the RCM process can indicate the

manufacturing process problems using appropriate field operational failure data and

Root Cause Analysis. Critical To Quality (CTQ) failures can be identified easily if the

data collection and FMECA document is described separately as it is quantitative rather

than qualitative. Basics of FMECA are component identification of the system, data

collection from functional structural diagram of the system, failure modes generation,

physical requirement description and the criticality concept development (Igba, et al.,

2013).

Defence Standard of 00-45 also requires that the FMECA should be implemented to

identify all asset failure modes (New, 2012). Selvik & Aven (2011) has used RCM-

adjusted FMECA worksheet and RCM logic diagram on their methodology. FMECA on

the RCM process can indicate the manufacturing process problems using appropriate

field operational failure data and Root Cause Analysis. Critical To Quality (CTQ)

failures can be identified easily if the data collection and FMECA document is

described separately as it is quantitative rather than qualitative. Ahmad, et al. (2012)

have practiced a methodology that uses the FMECA as a the prior classification of data

and for determination of external factors.

HAZard Identification (HAZID) model can help on early identification of hazards and

warnings (Paltrinieri, et al., 2013). McCoy, et al. (2000) has developed an innovative

way of enhancing the performance of HAZID models using case studies and feedbacks.




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HAZard and OPerability (HAZOP) is technique used since past 40 years for

identification of hazards on complex manufacturing processes and systems (Marin &

Toral, 2013). Marin & Toral (2013) have performed HAZOP study on the safety of

Mexican Oil & Gas industry.(Rabiei & Modarres, 2013) has selected acoustic emission

monitoring as an appropriate technique for monitoring the crack growth on aluminium

structures. Jamalabadi (2013) has implemented infrared camera to evaluate thermal

loading of thin carbon-steel plates. Mazza, et al. (2014) have developed an automatic

MCDM tool for solution ranking of network loss scenarios. Dhouib (2014) have

selected MCDA to waste tire logistic selection process. Tang, et al. (2014) have

developed an innovative fault diagnosis system using Shannon wavelet support vector.

Fault Tree Analysis (FTA) and Bayesian Belief Networks (BBN) are the other major

tools used in industry for maintenance management. Cai, et al. (2013) have created a

methodology that converts dynamic fault tree gates into dynamic BBN automatically.

Weber, et al. (2012) illustrate increasing trend of BNN application on dependability

structures and risk analysis. Qualitative part on the study by Trucco, et al. (2008)

determines casual dependencies between different events and their quantitative part

using the combination of FTA and BBN methodologies together. Cai, et al. (2013) have

also created a methodology that converts dynamic fault tree gates into dynamic BBN

automatically. Poropudas & Virtanen (2011) have used Dynamic BBN on decision

making process of their methodology.

Monte Carlo simulation helps to evaluate relevant system operational aspects using an

analytical model. Monte Carlo simulation can be time consuming but not when

assessing the availability of predetermined maintenance strategies (Marquez & Iung,

2007). Weibull’s distribution model on the methodology developed by Guo, et al.,

(2009) uses the Monte Carlo simulation in order to analyse its uncertainties.

Distribution of probabilities and consequences of events on the LNG tankers’ case study

by Montewka, et al., (2012) have been also analysed using Monte Carlo simulations.




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Analytic Hierarchy Process (AHP) can solve the multi-criteria decision problem by

pairwise comparison of each criterion by their weights using two major approach of

eigenvector and geometric means solution. Heo, et al. (2012) has implemented Benefits,

Opportunities, Costs and Risks (BOCR) on fuzzy AHP to make decisions on the best

hydrogen energy system infrastructure. Liu, et al. (2012) introduced Three different

vulnerability levels on their AHP neural network analysis and decision making. . Lee, et

al., (2012) have focussed on the use of fuzzy group AHP and Rough Set Theory (RST)

on selecting and evaluating New Service Concept (NSC) by modelling MCDM on their

research. Baserba, et al. (2012) have created a costomised Multi-criteria decision

analysis (MCDA) for appropriate design criteria option selection. Dhouib (2014) have

selected MCDA to waste tire logistic selection process.

Strengths, Weaknesses, Opportunities and Threats (SWOT) usually illustrate internal

factors (Strength and weaknesses) and External Factors (opportunity and threats from

market) on a single framework (Gorener, Toker, & Ulucay, 2012). Swot can create the

foundation for MCDM (Gao & Peng, 2011). Seker & Ozgurler (2012) have looked into

the implementation of SWOT with AHP on The Turkish electronics industry. Gorener,

et al. (2012) have introduced a SWOT analysis system that uses both AHP and MCDM.

Mohammadpur & Tabriz (2012) have performed SWOT analysis for in Petrokaran

factory in Iran. They also used fuzzy logic for analysis of uncertainties. Mohammadfam,

et al. (2012) have looked into safety problems of Tehran water treatment plant using

HAZOP model. Marin & Toral (2013) have performed HAZOP study on the safety of

Mexican Oil & Gas industry.




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3 SHIP MACHINERY STATE-OF-THE-ART

This section will review the applicable machinery maintenance and inspection research

in industry for INCASS machinery maintenance planning system. This would start with

previous research activities performed by classification societiesmaintenace

management and machinery condition monitoring methodologies. Then, it will continue

with industrially available condition monitoring tools and approach for machinery such

as Vibration analysis, Thermography and Lub Oil analysis. Finally, it will represent

additionall related machinery maintenance techniques and relevant research projects.

3.1 Classification Societies’ Input

Regardless of whether CM data is monitored on-line or off-line the host computer

system should provide common data processing and database storage. Data processing

will typically include automatic assessment of all monitored data against corresponding

reference levels. All exceptions should be highlighted via on screen displays and

exception reports.

The CM system software should also provide a comprehensive range of graphical

displays to facilitate data review and equipment diagnosis. Typical displays will

include:

Trend plots

Frequency spectra

Historical spectra (Waterfalls)

Time waveforms

Shaft orbits

User selectable X-Y plots

Gas turbine temperature spread plots

Compressor and pump efficiency maps, based on equipment manufacturer’s data




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Measurement point configurations

and, for transient vibration monitoring:

Bode plots

Polar plots (Nyquist plots)

Frequency spectrum cascade plots

All CM systems provide the means to export data, e.g. using export files and emails.

The more comprehensive systems now support full remote access, usually via dial up

lines and the internet. Such facilities can be invaluable for obtaining expert support and

assistance when diagnosing equipment problems.

3.1.1 Integration with Maintenance Management

Management and control of CM should be centred on the maintenance management

system (CMMS). This means that all data collection tasks should be scheduled from

the CMMS. These tasks should remain open until data analysis and reporting have been

completed. Responsibility for managing all aspects of CM related tasks should be

clearly defined. Agreement on and planning of any subsequent condition-initiated work

will typically involve members of the team. Whatever tasks are required should then be

scheduled using CMMS. A typical CM/CBM process flow diagram is presented in

Figure 4 overleaf.

When corrective work is completed it is important to obtain and document feedback on

the findings. If the defect was diagnosed correctly, and has subsequently been fixed, this

is clearly a success for CM. This should be made visible as it helps promote awareness

of the benefits of CM and the whole CBM process. Conversely if no defect is found, or

if a defect has developed which CM has failed to detect, then it may be necessary to

modify the CM strategy, or resort to supplementary preventive maintenance techniques.

When assessing the merits of CM it is important to realise that CM does not prevent




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anything going wrong – it only provides the information to support planned corrective

maintenance activities.

The key to success is the identification and monitoring of suitable benefit indicators,

which demonstrate the value being achieved. Furthermore CM cannot detect every type

of failure, and there is no feasible or effective response when any failure occurs

instantaneously. Unless this is appreciated, CM can end up being the scapegoat for all

sorts of issues. It is therefore important to fully appreciate, for each item of equipment,

those potential failure modes that can be reliably detected by CM, and by inference,

which failure modes cannot be detected by CM, and hence may require supplementary

interventive maintenance.

3.1.2 The P – F Interval

The underlying principle of CM/CBM is that failure modes give some sort of warning

that they are in the process of occurring or about to occur. If evidence can be found that

something is in the final stages of failure, it may be possible to take action to prevent it

from failing completely and/or to avoid the consequences. The P – F Interval is defined

as the interval between the detection of a potential failure and its decay into a functional

failure. The P – F Interval therefore defines how often the detection task, i.e. CM data

collection, must be completed if a functional failure of the equipment is to be avoided.

Basically CM data collection must be completed at intervals less than the P – F Interval.

In practice it is usually acceptable to select a frequency equal to half the P – F Interval.

This ensures that the monitoring will detect the potential failure before the failure

occurs, whilst providing a reasonable amount of time to do something about it.

In practice the P – F Interval does not follow a smooth curve, as shown in Figure 1, and

clearly will vary for different failure modes – from seconds to years. As stated in

Section 1, CM can only be applied to progressive, wear related failures, which typically

means a P – F interval of weeks or longer. Fortunately, many common equipment




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defects/failures, as considered for the development of FSMPs in Section 5, fall into this

category.

3.1.3 On-Line vs. Off-Line CM Systems

In practice CM will be implemented using a computer based system to acquire and

manage data, and report condition information. CM systems will either be off-line, on-

line or a combination of both. In the context of CM off-line monitoring involves

periodic manual data collection, usually with a battery powered portable data collector

(PDC). The typical PDC permits direct measurement of vibration, using a portable

accelerometer, direct connection to output signals from existing installed transducers,

and manual input, via a keypad, of visual readings from gauges and monitors. The PDC

is normally downloaded with a pre-defined sequence of equipment and associated

measurements. Frequency analysis of dynamic signals, such as vibration, is completed

within the PDC, and all data is stored in onboard memory prior to uploading to the host

computer. The host computer software provides all database storage, data management,

display and reporting functions.

On-line monitoring uses a permanent, hardwired system to automatically acquire,

process and store defined CM parameters. Typical systems consist of front end data

acquisition hardware, managed and supported by a host computer system. In addition to

acquiring data directly from installed transducers, such systems will often read digital

data directly from other control and monitoring systems, such as the plant DCS.

Communication between different elements of the system is usually achieved via

dedicated networks or serial links. All proprietary on-line CM systems also support off-

line monitoring.

The use of the terms off-line and on-line in the context of condition monitoring may be

different to their use by others, especially electrical engineers, to whom on-line means




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‘running’ and off-line means ‘shut down/turned off’. The relative merits of off-line and

on-line CM systems may be summarised as follows:

Off-line CM systems Advantages:

Inexpensive to purchase

Relatively straightforward to setup.

Effective, subject to P – F interval

Data collection regularly takes operator to equipment areas, where other

problems may then be identified, e.g. leaks

Flexible, can easily be extended to additional equipment.

Adaptable across a range of CM techniques

Reliable

Operate on standard Windows based PCs, easily updated

Off-line CM systems Disadvantages:

Implementation requires skilled operators

On-going operator (man time) costs

Only normally suitable for failure modes with a P – F interval exceeding 1

month

No advanced vibration analysis capability

On-line CM systems Advantages:

Provide significantly increased warning of lead time to failure (P – F intervals

of 1 hour or less, but see Cons)




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Lower operator (man time) costs

Can provide advanced vibration analysis capabilities, e.g. shaft orbits, transient

capture

Comprehensive remote data access capabilities are normally included

On-line CM systems Disadvantages:

Expensive to purchase and setup

On-going vendor support usually required

More prone to reliability problems, e.g. instrument faults, false alarms etc

Overall response time to potential failures is dictated by operator response to

alarms and initiation of subsequent actions

Danger of encouraging a fit and forget attitude, operators may not visit

equipment areas as regularly

Require specialist, higher spec computing platforms

In practice the choice between off-line and on-line CM systems is determined by two

factors:

i. The skill levels of the core operating crew.

If the skill level of the operators is low, off-line monitoring is not usually a viable

option, especially for high criticality equipment. In these situations operating

companies generally invest in comprehensive on-line systems, which will support

remote access by CM specialists from anywhere in the world.

ii. The remoteness of the location.




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If the plant is in a remote location, a long distance from operating company and vendor

support bases, logistics management of maintenance campaigns, spares, transport etc

becomes critical. The maximum warning of potential equipment problems is therefore

required, and this is a strong justification for installing on-line monitoring, especially on

high criticality equipment. In practice, remote locations often have a lower skilled local

workforce, from where the core operating crew must be drawn, and this further

reinforces the case for on-line monitoring.

The above factors explain why, with few exceptions, the majority of CM on offshore

North Sea assets and onshore E&P assets in the UK is undertaken using off-line

systems. In contrast, current oil and gas developments in the Sahara desert and off the

coasts of Nigeria and eastern Russia are being specified with comprehensive on-line

CM systems for all critical equipment.

This section aims to layout the motivation for Classification Societies data collection

activity, in reference to machinery and equipment, as well as the level of detail

monitored and how this information is collected. The research and requirement

identification takes place independently for each ship under consideration; hence

Tanker, Bulk Carrier and Container ship. Furthermore, a review on condition

monitoring standardization rules from the Classification Societies’ point of view is

considered for the final selection of critical ship machinery systems.

3.1.3 Motivation with respect to Machinery Maintenance

The role of Classification Societies is to check that safety standards of ships are met

throughout surveys, inspections, tests and controls. As long as ship machinery and

equipment monitoring technologies provide relevant data and information that can

demonstrate that condition of equipment is acceptable to ensure ship safety, they can be

used as a complementary means for Classification Societies to confirm that machinery,

equipment and appliances comply with the applicable rules and remain in satisfactory




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condition. Moreover, when Condition Monitoring (CM) techniques are properly

applied, they can enhance decision support and facilitate the work of Class surveyors,

thus they can get an objective opinion on the condition of a surveyed item/system of

machinery and equipment without dismantling it.

The entire control over a vessel is managed by the shipowner or/and ship operator,

including the manner in which it is operated and maintained. In this respect, ship

Classification depends on the shipowner/operator, who by operating in good faith will

disclose any damage or deterioration that may affect the vessel’s Classification status to

the Class Society. If there is any doubt regarding the above, the owner should notify the

Class and schedule a survey to determine if the vessel complies with the relevant Class

standards.

Classed ships are subject to surveys to continue being in Class. These surveys related

with machinery and equipment include the Class renewal (also called “Special

Survey”), Intermediate Survey and the Annual Survey. They also include the tailshaft

survey, boiler survey, machinery surveys and surveys for the maintenance of additional

Class notations, where applicable. Therefore, a Class surveyor may only go on board a

vessel once in a twelve-month period, for the annual survey. At that time it is neither

possible, nor expected that the surveyor scrutinize the entire structure of the vessel or all

of its machinery. The survey involves a sampling, for which guidelines exist based upon

empirical experience, which may indicate those parts of the vessel or its machinery that

may be subject to corrosion, or they are exposed to the highest incidence of stress, or

may be likely to exhibit signs of fatigue or damage.

The surveys are to be carried out in accordance with the relevant Class requirements in

order to confirm that the condition of machinery, equipment and appliances complies

with the applicable rules. A Classification survey is a visual examination that normally

consists of:

an overall examination of the items for survey




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detailed checks of selected parts

witnessing tests, measurements and trials where applicable

When a surveyor identifies defects or damage to machinery and/or any piece of its

equipment, which in the opinion of the surveyor affects the ship’s Class, remedial

measures and/or appropriate recommendations/conditions of Class are to be

implemented before the ship continues in service.

In this respect, the ISM Code clarifies that the ship operator (the “Company”) is

responsible for ensuring the safe and pollution-free operation of the ship. In particular,

the Company is required to ensure that the ship’s machinery and equipment are

maintained and operated in accordance with the applicable rules and regulations and

any additional requirements that may be established by the Company. Paragraph 10.1 of

the ISM Code states, “The Company should establish procedures to ensure that the ship

is maintained in conformity with the provisions of the relevant rules and regulations and

with any additional requirements which may be established by the Company”.

The procedures should be documented, and should ensure that applicable statutory,

Class, international (e.g. SOLAS, MARPOL) and port state requirements are met, and

that compliance is maintained in the intervals between third-party surveys and audits.

The maintenance procedures should also include any additional requirements

established by the Company. These may arise, for example, from an analysis of the

previous maintenance files of ship’s machinery and equipment, from the particular

demands of ship’s operations, or from manufacturers’ recommendations. Classification

Societies audit as Recognised Organisation for the existence of such a system.

However, data is not shared among the various stakeholders.

The scope of equipment on which condition monitoring is applied is not fixed by the

Class Society, while the ship operator decides which equipment needs to be monitored.

For a standard PMS scheme (IACS, 2014), the Class Society concerns are to ensure that

the maintenance recommendations from supplier/manufacturers’ manual are respected.




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If the ship operator decides to postpone a planned maintenance task/overhaul based on

condition monitoring results, the Class can accept the postponement under certain

circumstances. The different survey techniques that can be applied are defined in IACS

URZ 20 (IACS, 2014):

Continuous Machinery Survey: overhauls based on calendar time

Planned Maintenance Scheme: overhauls can be based on running hours of

machinery in normal operation or on condition monitoring by analysing the

trend of significant parameters (vibrations, temperature, pressure, etc.)

The survey scheme may be a combination of the above and must be approved by the

Class Society. Classification Societies can moreover provide guidance on the

implementation and use of Condition Monitoring techniques in order to establish a

recognized practice onboard ships. Their Rules generally provide their own list of

equipment whose condition can be monitored (i.e. electric propulsion motor main diesel

engine) as part or independently from the Planned Maintenance Survey (PMS) scheme.

Minimum parameters to be checked (vibration, temperature, exhaust gas temperature

etc.) for each piece of equipment are agreed with the owner after assessment of the

equipment that is to be included under such a regime. The motivation for data collection

by Classification Societies is laid out summarised as Class Survey and Statutory

Survey. The information collected during these surveys is kept within the Classification

Societies database system, however it is owned by the owner of the vessel.

The resolution of failures recorded is expected to be more granular than failure

information held by the owner/operator. The main reasons for this are the following:

As a Class surveyor may only go on board a vessel once in a twelve-month

period and Classification depends on the shipowner/operator operating in

good faith by disclosing to the Class society any damage or deterioration that

may affect the vessel’s Classification status.




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Information is held on failures that are known to Class either having been

found during survey or having been reported by the owner. This is a subset of

all failures on a vessel; the failure is described with remedial measures and/or

appropriate recommendations/conditions of Class are to be implemented

before the ship continues in service.

A cause of failure may not be properly recorded as an in depth analysis of

cause of failure during a survey may not be possible.

3.1.5 BV - Condition Monitoring

BV Rules for the Classification of Steel ships as in Part A, Chapter 2, Appendix 1 and

Article 6 of BV, 2014 mention with the Requirements for Machinery items surveyed

based on condition monitoring embedded in the Planned Maintenance Survey Scheme.

The extent of condition-based maintenance and associated monitoring equipment to be

included in the maintenance scheme is decided by the Owner. The minimum parameters

to be checked in order to monitor the condition of critical main and auxiliary machinery

are provided, contributing to the final condition monitoring selection tools. These

systems are grouped in items including main systems such as electric propulsion motor,

main diesel engine, main and auxiliary steam turbines, auxiliary diesel engines, as well

as auxiliary systems such as cooling, heating, pumps and filters. With reference to the

main diesel engine the parameters to be checked are the following (section 6.1.3, BV

2014):

power output

rotational speed

indicator diagram (where possible)

fuel oil temperature and/or viscosity

charge air pressure




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exhaust gas temperature for each cylinder

exhaust gas temperature before and after the turbochargers

temperatures and pressure of engine cooling systems

temperatures and pressure of engine lubricating oil system

rotational speed of turbochargers

vibrations of turbochargers

results of lubricating oil analysis

crankshaft deflection readings

temperature of main bearings

In addition to the above, more details and indicative information on the main and

auxiliary systems examined as per BV rules are included.

3.1.6 LR - Condition Monitoring

LR Rules Part 5 Chapter 21 (LR, 2014a) deal with the Requirements for Condition

Monitoring Systems and Machinery Condition-Based Maintenance Systems. An

operator can choose to apply for a number of LR Class notations as appropriate to their

needs. If Machinery Condition Monitoring (MCM), Reliability Centred Maintenance

(RCM) or Machinery Condition Based Maintenance (MCBM) is selected, Machinery

Planned Maintenance Scheme (MPMS) is also required as knowledge of the planned

maintenance systems is a critical element and must be considered during approval of the

scheme. LR’s ShipRight Procedures for Machinery Planned Maintenance and Condition

Monitoring contain the following notations:




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Approved Machinery Planned Maintenance Scheme (ShipRight MPMS

Descriptive Note)

Machinery Condition Monitoring (ShipRight MCM Descriptive Note)

Machinery Condition Based Maintenance (ShipRight MCBM Descriptive Note)

Reliability Centred Maintenance (ShipRight RCM Descriptive Note)

Screwshaft Condition Monitoring (ShipRight SCM Descriptive Note)

Turbine Condition Monitoring (ShipRight TCM Descriptive Note)

Furthermore, it also provides guidance on typical shipboard machinery and suitable

Condition monitoring techniques. (LR, 2014b). The selection of which specific

Machinery and Equipment items are to be covered by the notation is the responsibility

of operators, who will apply for the relevant notation. In addition to the above, the

operator may include additional non-Class items in the maintenance plan but not

necessarily the survey plan and vice versa as the strategy regarding the ship

maintenance and Classification may not be completely aligned. This will depend on the

particular operator and the needs related to a particular ship maintenance.

3.1.7 RINA - Condition Monitoring

RINA Rules 2014 for the Classification of Ships as in Part F, Chapter 1, Appendix 7

and Section 6 deal with the Requirements for Machinery items surveyed based on

condition monitoring in the Planned Maintenance Survey Scheme (RINA, 2014).

The selection of the items to be included in the CBM program is up to the Owner. The

frequency of condition monitoring measurements can be increased according to the

criticality of the equipment. In general, the CBM strategy and its extent, inclusive of the

acceptability limits, are to be approved by the Manufacturer. CBM techniques not




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included in this section may be accepted if they are proposed or established by the

Manufacturer of a machinery item. Guidance on CBM can be found in the Society

"Guide for the Application of Condition Based Maintenance in the Planned

Maintenance Scheme" (RINA, 2014).

In the Rules, a minimum set of data is established for most machinery items that can be

usually found onboard, which may also include other types of condition monitoring

parameters and techniques if they are proved to be of equivalent or better standards to

the existing ones. It should be noted that, notwithstanding CBM parameters given for

internal combustion engines, such equipment is not the preferred choice for the

application of CBM by Owners as per the RINA experience. This is due to main

engines and diesel generators being critical items in terms of safety and financial

aspects. Furthermore, machinery and equipment manufacturers are quite strict on the

maintenance schedules they provide for the above items, therefore they are reluctant to

waive relaxations unless CBM is carried out by themselves (obviously bearing an

associated cost per machinery and equipment item monitored).

Summarising the above, Appendix IV provides a small extract of ship machinery and

equipment systems onboard ships as well as the minimum requirements for Condition

Monitoring involving details on Diesel engines (single or dual fuel) for direct main

propulsion and Diesel engines for electric power generation.




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3.3 Machinery condition monitoring tools

3.3.1 Vibration monitoring

The various vibration frequencies in a rotating machine are directly related to its

geometry and operating speed. By knowing the relationship between the frequencies

during optimal operation of machinery, it is easy to identify at an early stage any types

of defects and take remedial action well before the equipment is damaged or has to be

taken out of operation. Furthermore the history of the machinery and its degradation

pattern can be used to know the evolution of the defect. Among others, vibration

monitoring can detect unbalanced rotating machinery parts, excess sleeve or bearing

wear, misalignments, damaged gear teeth, damaged bearings, etc. This technique

applies also to Propulsion Diesel engines, Electrical Generator engines, Gear boxes,

Main steam turbines, Pumps and Motors Compressors, Turbochargers, Generators,

Propellers and Shafting& waterjets, covering most equipment onboard a ship. It has to

be noted that a number of structural defects have their origin in excessive vibration from

machinery, as mentioned above, so its early identification and rectification ensures the

good condition and longevity of associated structures. All the sensors used to measure

vibration, convert the physical magnitude (in terms of displacement, velocity or

acceleration depending on the kind of sensor) into a proportional electrical signal that

can be split into its fundamental frequencies.

Vibration monitoring involves the acquisition of vibration data, which can then be

checked for trend over a period of time. It focuses more on detecting changes in

vibration behaviour rather than measure any particular behaviour in isolation. Vibration

measurements for CBM purposes may vary from simple to complex and can include

continuous or periodic measurements. Spectrum analysis is generally more suitable to

steady state conditions, whilst waveform analysis is more suitable to transient

situations. Other proprietary techniques are more useful to detect very specific failures,

like wear or insufficient lubrication of roller bearings or gears




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Vibration monitoring systems can be made of instruments permanently installed on

machines (with continuous of periodical data reading) or portable instruments used to

record data manually at preselected locations on a machine at periodic intervals using

portable tools for spot measurements. For consistent and comparable results,

measurements should always be taken under operating conditions that are as close as

possible to those that may be considered as ‘normal’ for the machine (‘baseline’

conditions), including meteomarine conditions.

3.3.1.1 Vibration Parameters

The main types of vibration measurements that are normally used for CBM of shipboard

rotating machinery are:

(a) Vibration measurements made on the non-rotating structure of the machine, such as

bearing housings and casings: the typical parameter is root mean square (r.m.s.) velocity

in units of millimetres per second (mm/s). For gearing and high speed machines (steam

and gas turbines), peak acceleration is also often used and expressed in units such as

metres per square second (m/s2) or in terms of ‘g’, the acceleration due to gravity (9.81

m/s2).

(b) Relative motion between a rotor and the stationary bearing housings (typically peak

or peak-to-peak displacement is measured, in µm).

As mentioned above, special techniques are often used for the CBM in rolling bearings

to integrate the more general vibration monitoring. Various techniques, such as shock

pulse analysis (SPM™), Spike energy™, Kurtosis factor and Acceleration crest factor

can be used to indicate the status of the bearing.




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3.3.1.2 Vibration Measurements

As far as possible, vibrations should be measured on bearing housings and never at

freely vibrating surfaces. Measurements should normally be taken in axial and radial

planes with reference to the shaft axis and always in the direction of minimal stiffness

of the structure. If the bearing housing is not directly accessible, measurements should

be taken on the nearest part of the adjacent structure that is rigidly connected to the

bearing. Intrinsically safe equipment should always be used in explosive environments

(the pump room in an oil tanker, for example).

3.3.1.3 Standardization of the measurement

Since CBM is based on the assessment of the trend of the measured values over time, it

is imperative that the acquisition of such measures be carried out by a procedure as

standard as possible:

To facilitate consistency, the measurement points for portable monitoring

systems should be clearly marked and identified using a consistent convention.

In particular, to avoid errors in the identification of the point, it is suggested to

utilise systems to automatically identify the measurement points, such as bar

code placards, frequency radio transponders or similar devices that can be read

by the portable instruments.

Repeatable and accurate vibration measurements on stationary parts require

adequate contact between the transducer and the vibrating surface. Fixed

transducers may be mechanically connected or bound to the machine in such a

way as to avoid that they provide unreliable measures because of undue stress or

motions. If portable systems are employed, it is to be ensured that a positive

means of contact is used. The most common types of transducers used for

vibration monitoring are:




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(a) Accelerometers, the outputs of which can be processed to give any of the

three vibration parameters, acceleration, velocity or displacement.

(b) Non-contacting proximity probes, whose output is directly proportional to

the relative displacement between the rotating and non-rotating elements of

the machine.

As to Type (a) transducers, it is suggested to use accelerometers having a

minimum sensitivity of 100 mV/g, to guarantee the solidity and the reliability of

the application.

In the configuration of the vibration measurement acquisition, it is suggested the

selection of a range 2-1000 Hz with a minimum resolution of 1600 lines for

rotating machinery with speed up to 3500 rpm, or range 25000 Hz with a

minimum resolution of 3200 lines for machinery rotating at higher speed.

It is recommended also to employ the Hanning type window that allows an

optimal proportionality of the vibration amplitudes to the various frequencies in

the standard applications.

To facilitate consistency, it is also important that, as far as possible, the

measurement be carried out always in the same operating conditions; should this not

be fully practicable, it is necessary to record parameters suitable to give indications

of the operating conditions of the machine at the moment of the measurement (e.g.

% of load, absorbed power, flow, pressure etc.).




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3.3.1.4 Calibration

Calibration of the instrumentation used for CBM measurements must be carried out

annually by the manufacturer or by an authorized service supplier.

In general, a ± 10% tolerance for the required amplitude and frequency range of the

measurement is acceptable.

3.3.1.5 Baseline Measurements

By ‘baseline vibration data’ it is intended those data obtained when the machine is

operating at its predominant (i.e., most commonly employed) load conditions in a stable

and acceptable manner. All subsequent measurements will be compared to these

baseline values to detect vibration trends. For new or freshly overhauled equipment, an

initial operational time period (break-in) should be allowed before baseline

measurements are taken. After break-in, baseline data for a piece of equipment in steady

state operation can still be acquired and used as a reference point to detect future

changes.

Baseline data of a piece of equipment should consist of a comprehensive set of

measurements necessary and sufficient to define its vibratory profile. Even for baseline

data acquisition it is necessary to record parameters suitable to give indications of the

actual operating conditions of the machine at the moment of the measurement (e.g. %

load, absorbed power, flow, suction and delivery pressure, shaft rotational speed etc.).

Subsequent periodic measurements need only be sufficient to detect changes and, if

deemed necessary, the baseline measurement procedures may be repeated to help

determine the cause of the changes.

Machine characteristics such as shaft speeds, bearing and gear geometry, coupling and

foundation type, model, serial number, capacity, electric motor power, number of motor

poles, etc. should be recorded to enable detailed analysis of the vibration data.




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3.3.2 Vibration Analysis - broadband vibration

The two most common types of vibration analysis are broadband vibration and

frequency spectrum analysis, which are described in the following items.Broadband

vibration measures the total energy associated with all vibration frequencies generated

at a particular measurement point. Values of broadband vibration can be compared to

baseline measurements, assessed against vibration standards or alarm set points and

displayed in trend plots to graphically show changes in machine condition over time.

Various International Standards (like ISO Standards 10816, ISO 7919, 13373-1) specify

the acceptable broadband vibration values for different types of machines. The

following table, obtained from the aforesaid sources, provides the vibration limits of

rotating machinery (e.g., centrifugal pumps) driven by separate electric motors of

various sizes.

3.3.3 Vibration Analysis - Vibration limits for electric motor

driven rotating machinery

Table 1 - Vibration Limits for Electric Driven Rotating Machinery

Ship Machines

< 15 kW Limit

(mm/sec rms)

Ship Machines

15 - 75 kW Limit

(mm/sec rms)

Ship Machines

> 75 kW Limit

(mm/sec rms)

Rigid Foundation

Ship Machines <

15 kW Limit

(mm/sec rms)

Flexi Foundation

Good 0.7 1.1 1.8 2.8

Satisfactory 1.8 2.8 4.5 7.1

Unsatisfactory 4.5 7.1 11.2 18.0

Excessive > 4.5 > 7.1 > 11.2 > 18.0




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Table 1 illustrates the vibration for electric driven rotating machinery. However, such

absolute limits, although set by International Standards, are not related to the operating

environment of the machinery. Therefore, they should be used as guidance, and CBM

should be mostly based on the rate of change of vibration levels rather than on singular

values and trend plots are used to present this information. The following figure 2

shows a typical trend plot for a motor driven pump.

Figure 2 - Typical plot of vibration readings

3.3.4 Vibration Analysis - Frequency Spectrum Analysis

Because different types of machinery problems generate vibration at different

frequencies, it is very useful to break down a vibration signal into individual frequency

components. The amount of vibration occurring at any particular frequency is called the

amplitude of vibration at that frequency. A plot of amplitude against frequency is called

a frequency spectrum, sometimes known as a ‘vibration signature’. Frequency is

generally measured in cycles per second (Hertz, abbreviated to Hz), cycles per minute

(cpm) or Orders, where:




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Order = Frequency of vibration in cycles per minute/Rotor speed in revolutions per

minute. Figure 3 shows a typical frequency spectrum.

Figure 3 - Typical vibration signature

Frequency spectrum displays are very useful in evaluating machinery condition. High

vibration levels at certain orders of the rotational speed are generally indicative of faults

and can be used as an aid to fault diagnosis. Some of the common faults in a rotating

machine are:

- Wear

- Imbalance

- Misalignment

- Looseness

- Bearing damage

- Resonance

- Fatigue




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- Deformation of a shaft

- Defects in transmission means

- Cavitation.

3.3.5 Vibration Analysis - Minimum Technical Characteristics of

the Measurement Instrumentation

To diagnose the operating conditions of machinery with accuracy, the following

features are imperative:

- frequency range of operation sufficiently wide as to include the typical

frequencies of the most common mechanical defects related to the machinery

being monitored: the instrumentation can be considered satisfactory if it can carry

out measurements from a minimum of 3÷100 Hz up to at least 3÷5 kHz

- resolution such as to allow to ascribe the frequency to the corresponding

mechanical defect, in an accurate and unambiguous manner; for this purpose, the

minimum satisfactory resolution is of 3200 lines.

In any case, to obtain significant trends at least four measurements per year should be

taken at sufficiently regular intervals. A higher frequency should be established on the

basis of the criticality of the machine, in terms of safety and/or economy.

3.3.6 Thermography

This technique measures absolute or relative temperatures of the different parts of the

machine. Abnormal temperatures indicate developing problems. Any friction or

coupling problem generates overheating and any increment in the electric resistivity

results in “hot spots”. It is commonly used as a complementary technique to confirm or




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support the diagnostic made with others approaches. The most common contact

methods use thermocouples and thermometers. However non-contact methods using

infrared sensors have become a desirably alternative over the conventional ones. This

technique can be applied to Steam turbines, Turbochargers, Diesel engines, Electrical

engines, Generators.

When a scan performed with the infrared camera reveals a potential problem,

thermogram and temperature are to be taken. Absolute surface temperature

measurements of the target, ambient or background measurement are to be detected

within the surrounding target area to indicate relative temperature rise. A thermogram is

known as an infrared photograph and is obtained by videotaping the image from the

infrared camera. A means of assessing severity of temperature in assessing the

maintenance scheduling is presented in the chart below. The degree of temperature rise

and criticality of particular equipment or process involved should determine final

decision as to priorities and order of maintenance (Table 3).

Table 2 - Temperature to Maintenace Scheduling Relation Table

TEMPERATURE RISE REMARKS

1° - 10°C Corrective measures required at the next

scheduled maintenance period

10° - 20°C Corrective measures to be scheduled on a priority

basis

20° - 30° C Corrective measures required as soon as possible

Above 30° C Corrective measures required immediately

3.3.7 Lubricating oil analysis

This tool is aimed at controlling the state of the lubricant, the level of degradation of the

different components of the machine and the presence of moisture and water by




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analysing the lubricating oil. Its specific application to diesel engines, compressors and

gear boxes, besides to the advantages related to the optimization of the lubricant oil

changes, can confirm the diagnostic made with others tools, in particular with vibration

monitoring. This technique monitors a number of parameters, giving an early indication

of potential damages and malfunctions. Lubricating oil analysis can be applied to Diesel

engines, Gear boxes, Steering Gears, Compressors and Steam turbines among others.

The sample must be kept free of contaminants and must belong to the oil actually in

contact with the lubricated parts. The analysis is to be performed by the equipment

supplier or by specialized laboratories authorized by the supplier. Table 4 provides the

general correspondence between the parameter and the condition to be checked.

Table 3 - Parameter Condition Detection Table

Parameter Condition to detect

Viscosity Increase or decrease

Flash point Decrease

Water concentration Presence of salt or fresh water

Alkalinity Increase or decrease

Strong acid Presence

Acidity Increase

Insoluble substances Increase

Metals Increase

Microbial

concentration

104

Particle count (*) Increase

(*) particularly important for hydraulic systems with requirements of high

oil cleanliness

As an alternative to lube oil analysis for Pods and Gas Turbines, a fixed analyzer

allowing a continuous oil debris monitoring can be fitted in the section from the oil

return line to the filter, provided that it does not affect the oil flow by any means.




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The working principle of such devices is based on the detection of the mass of metal

particles (ferrous and non-ferrous) in the lube oil flow, through the variation of the

magnetic field caused by the passage of the metal particles through a coil, as shown in

Figure 4:

Figure 4 - Mass of Metal Particle Detection in Oil

Every particle has a different coupling level with the magnetic field when it crosses the

sensitive zone, and this turns into a characteristic output signature, as shown in the

Figure 5:

Figure 5 - Ferromagnetic vs. Non-Ferromagnetic

The amplitude and the phase of the output signal are used to identify the size and the

nature of the particle. The amplitude is proportional to the particle mass for

ferromagnetic materials, and to the surface area of the particles for non-ferromagnetic




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conductive materials. The signal phase of ferromagnetic materials is opposite to that of

non-ferromagnetic materials.

The output signal is normally elaborated in a control unit that yields the following

information, which can be stored for subsequent analysis and record :

particle number and size

total mass

particle rate

comparison of the actual status with pre-set thresholds

alarm indications

distribution of the particle size .

3.3.8 Monitoring of combustion parameters

This technology basically consists on the collection and analysis of combustion pressure

peak values, the vibrations and ultrasound data of the cylinders on a diesel engine

onboard. The correct operation of the diesel engines is affected by a wide variety of

parameters: Temperature and pressure of the inlet air and exhausts, temperature and

pressure of the fuel, condition and characteristics of the cylinders, etc. Additionally, the

combustion pressure peak value in each cylinder is a key indicator of the engine

operation. An accurate evaluation of the combustion balance can be achieved by means

of the measurement of the time of ignition (the angle at which it is achieved),

combustion pressure peak values, and the pressure distribution during the whole

combustion cycle. This information will provide the basis for the optimum tuning and

operation of the engine that leads to fuel consumption reduction, and an accurate and

early identification of potential engine failures. This technique can be applied to both

propulsion engines and generators onboard.

3.3.9 Partial discharge measurement techniques




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This technique specifically conceived for electrical rotating machinery is aimed at

estimating the amount of defects in the isolation which permits to evaluate its level of

degradation. It consists of using coupling capacitors to measure the high frequency

signal characteristic of the gaseous ionization phenomena that occur within the voids of

the electrical isolation in the stator. This technique can be applied to generators and

electrical engines.

3.3.10 Current analysis techniques

Obtaining the spectrum of the stator currents permits to identify electromechanical

asymmetries in the rotor due to the breakage of the cage bars, degradation of the short

circuit rings and eccentricity of the air gap. This technique can be applied to electrical

engines.

3.3.11 Monitoring architecture topologies

There are different monitoring modalities depending on the criticality of the machinery,

environmental conditions, cost associated to a possible failure, etc. These can be

distinguished between Off-line and On-line monitoring. Off-line monitoring consists of

performance measurements on a periodic basis. The frequency sample is going to be

determined according to experience, information gathered initially, the trend observed

in the historic of the measurement, etc. On the other hand, On-line monitoring consists

of continuous measurements of the parameters selected to control the system by means

of the installation of collectors and its cabling to the acquisition system. Within this last

category it is possible to differentiate the following two modalities:




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Basic continuous protection system: These systems are made up of devices which

allow configuring different alarms or maximum levels for different frequency

bands. This characteristic is very important since every defect is associated with

one or several frequency bands. The simplest systems of this type consist of a

piezoelectric sensor that measures the global value, conditioning the signal by

them. There are also other devices capable of monitoring a reduced number of

channels in isolation showing the alarms through frontal leds, analogical output

and activation of relays.

Continuous monitoring systems: These systems collect the dynamic signal coming

from different sensors (especially from vibration sensors) and send the

information to a data server where this information can be analysed. According to

the type of acquisition, multiplex systems can be used, not acquiring

simultaneously data from all their channels; on the other hand, continuous

acquisition systems do so.

Depending on the logic arrangement of the components two modalities can be

distinguished: Centralised and distributed architecture system. In the centralised system,

all the signals are acquired and processed by a single unit. In the distributed architecture

system, there are acquisition units near the machinery to be monitored, thus achieving a

modular installation more suitable to be expanded and less dependent of the failure of

an acquisition unit. Finally the current distributed systems can be integrated with the

IPMS (Integrated Platform Management System) which integrate the monitoring and

control system of the propulsion, electric and auxiliary systems (Figure 6).




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Figure 6 - Diagram of the monitoring system

3.3.12 Other related methodologies

In addition to the above, the Reliability Centrered Maintenance (RCM) and Risk Based

Inspection (RBI) maintenance principles have been established in the offshore oil and

gas industry. Conachey and Montgomery (2003) described the applications of RCM in

the marine industry in order to determine the functions and the failures of a system as

well as its equipment which is considered as the best strategy to manage any failures

occurring and finally the requirements for spares. Serratella et al. (2007) also discussed

in their paper the RCM applications for the machinery and rotating equipment of ships.

RBI on the other hand is complementary to RCM in terms of dealing with the reliability

of structures, either ships or offshore vessels. In Straub et al.(2006) the RBI application

is presented regarding fatigue deterioration for offshore fixed steel structures and

floating, production, storage and offloading vessels (FPSO’s). Ku et al. (2004)

discussed the implementation of risk-based inspection plans regarding the strength and

fatigue assessment of a floating production unit (FPU) located in offshore West Africa

while Turan et al. (2010) also presented a methodology for examining the effects of hull

structure repairs on the life cycle cost of ships. In another paper by Lazakis et al (2010)




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the introduction of a new maintenance management approach is also introduced,

particularly referring in the appraisal of the reliability and criticality characteristics of a

vessel, thus increasing the vessel’s operational efficiency.

Regarding Decision Support Systems (DSS), they have been implemented with an

orientation of individual decision-makers. The DDS tool design can be combined for

sophisticated database management capabilities with access to internal and external

data, information, and knowledge; powerful modelling functions used by a management

system and powerful but simple user interface designs that are related with the

interaction between the system and the user. The implementation of a DSS for an Ice

Load Monitoring (ILM) system which can be installed onboard a ship is mentioned in

the literature (Richardsen, 2008). This includes items such as strain sensors to measure

the strain at the plates or frames (mostly in the specific area of bow), equipment to

measure the thickness of ice, the appropriate designed software with a computer to

evaluate data and display them at bridge of the ship, utility of meteorological and

satellite data and updating processes for ice information for ships operating at the same

route.

An integrated approach developed for supporting management in addressing

technology, organisation, and people at the earliest stages of manufacturing automation

decision-making is also presented in (Almannai, 2007). This concept combines both the

quality Function Deployment Technique (QFD) and the Failure Mode and Effects

Analysis (FMEA). The principal characteristics and functions of both techniques are

merged to form a decision tool. In the first case QFD identifies the most suitable

manufacturing automation and FMEA, identifying the risk in the manufacturing system

design and implementation phases. In this case FMEA highlights any related trade-offs

or areas of concern for extensive reviewing by identifying failures of products or

services and afterwards determining its frequency and impact.

3.4 State-of-the-art on Condition Based Maintenance (CBM)




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Condition Based Maintenance (or predictive maintenance) emerges as a response to the

problematic that arise in the use of other maintenance methodologies such as corrective

and preventive maintenance. It tries to avoid unnecessary inspections for machinery and

structure which is in a technically perfect condition and to minimise the amount of

unexpected machinery breakdowns and structure failures. This methodology seeks to

know the actual state of the machinery by means of the measurement and suitable

analysis of a list of specific variables and parameters without interfering in the normal

operation of the system. This leads to know if the system is working properly, estimate

when the failure is likely to happen and what the cause has been or will be, allowing to

programme the maintenance tasks, thus to reduce the cost associated to them, and to

minimize the inherent risks and not expected costs of an unforeseen machinery failure.

Apart from Navy vessels this technology is not widely used in the maritime sector. U.S.

Navy was a pioneer in this field. They performed studies in the 60’s which showed the

little correlation between preventive maintenance and reliability. As a result of these

studies, the US Navy implemented the predictive maintenance methodology instead of

the one based on predefined time intervals. On the other hand, this technology is a

standard in high-technological industrial sectors where, in most of them, safety is a

“sine qua non” requirement. Among others, this methodology is well extended in Power

Generation, Nuclear, Thermal, Hydraulic, Wind farms, Defence, Oil & Gas, Paper,

Cement and Petrochemical Industries, and also in other modalities of transport. In fact

there are several studies that integrate life-cycle concept with these maintenance

strategies in wind turbines (Andrawus, 2008), power generation plants (Back, 2010),

and even in nuclear applications (IAEA 2007).




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3.4.1 Theory Underlying the Determination of CBM Task

Intervals

Condition-Based Maintenance (CBM) task intervals, in theory, are determined based on

the expected P-F interval. The concept of P-F interval illustrates the relation between

the CBM task frequency and the mechanism of deterioration, but in practice it is often

impossible to obtain a true mathematical function that describes the process. What

follows was reported only qualitatively, for explanatory purposes.

Figure 8 shows this general process. It is called the P-F curve, because it shows how a

failure starts and deteriorates to the point at which it can be detected (the potential

failure point "P"). After this point, if the problem is not detected and corrected, the

deterioration keeps on (often at a higher rate) until it reaches the point ("F") of

functional failure, i.e. when the item ceases to perform its defined function. The P-F

interval is therefore the amount of time (or the number of stress cycles) that elapse

between the point P and the point F.

Figure 7 - CBM in different industrial sectors




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More specifically, the key concept of the machinery CBM methodology rests on that

every machine, when it works properly, has a determined level of vibration, noise or

whatever parameter, considered as “base state” and when a defect appears, even in early

stages, leads to a characteristic increment of the level which permits to identify it and

evaluate its severity. The experience has revealed that vibration monitoring is one of the

most powerful tools to control properly the condition of the rotating and oscillating

machinery. However other techniques have been developed, in some cases, to provide

support to the first one, in other cases to get new and necessary information, which

allows the identification of defects mainly in static equipment, electrical and internal

combustion engines.




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3.5 Other Condition Monitoring and Machinery Related state

Literature

Moreover, the SilenV project proposed a holistic approach to reduce ship-generated

noise and vibration pollution (Beltran, 2012). This project mostly focused on the Noise

and Vibration (N&V) present in vessels. Detailed measurements of the related

machinery noise and vibration were performed by using condition monitoring

equipment for the main engine and auxiliaries of the vessel, thus allowing for a detailed

mapping of the machinery condition under various operational loads. Related to SilenV,

AQUO (Achieve QUieter Oceans) is a recently funded FP7 project focusing on

obtaining an accurate description of the underwater radiated noise and on mitigation

measures to reduce it (AQUO, 2012). The final aim of the project is to provide practical

guidelines documents providing support to policy makers, acceptable by shipyards and

ship operators.

A diagnostic system for marine diesel engines introducing an expert system model is

presented by Charchalis and Pawletko (2011). The research sources are structured on

knowledge acquisition basis for diesel diagnostics undertaken from experts. El-Thalji

and Liyanage (2012) propose a review related with operational and maintenance

practices for power applications especially in wind energy industry by collecting,

categorising, analysing and linking the published literature and gaps among research

and commercial requests. The study outcomes are reviewed in sections such as site and

season disturbances; life cycle and stakeholder’s involvement; dependability and asset

deterioration challenges; monitoring, diagnostic, prognostic and information and

communication technologies (ICTs) applications and physical asset optimization.

Due to lack of shaft speed hence vibration signals, Cardona-Morales et al. (2013)

introduce a novel, robust and accurate Order Tracking (OT) system established on the

state space model avoiding speed reference signals. Similarly Sundstrom (2013) states

that CM of rotating bearings at lower speeds than 100rpm is challenging cause to the




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lack of useful signals produced from spalls and cracks as they are indicated from low

energy content. The author proposes a method of monitoring rotating elements allowing

the analysis of speeds within the 1-20,000rpm range using high performance low-noise

electronic components and extensive signal processing detecting even well-lubricated

bearings. In a recent study, Lamim et al. (2013) presents a vibration analysis for

mechanical fault errors featured by low isolation and unbalanced voltage in induction

motors.

According to Vervloesem (2013), ultrasounds are capable of detecting faults and

malfunctions on rotating machinery. Nevertheless on ships exists also non-rotating

equipment and through a research is explored the applicability, user-friendliness and

accessibility of this technology on non-rotational equipment breakdowns. The major

benefits of ultrasound application are the ease of manual data collection and the direct

result sourced from them. An unknown aspect of ultrasound condition monitoring

compared to traditional vibration analysis is the ability of performing on high-speed and

slow-speed rotating equipment too as low as 0.25rpm.

A significant reduction of one third in the long duration of refrigeration compressor

performance is achieved by Penz et al. (2012) using unsteady-state data analysis

through a hybrid Fuzzy-Bayesian network. However it is highlighted that performance

tests are experimental processes purposing to measure refrigerating capacity, power

consumption, isentropic efficiency and coefficient of performance (COP) scoping

research and development (R&D), establishment of catalogue parameters and quality

assurance.

Rafiul Hassan et al. (2012) present a model integrating Hidden Markov Model (HMM),

fuzzy logic and multi-objective Evolutionary Algorithm (EA) purposing the prediction

of non-linear time series data. Multi-objective EA purpose of finding a range of optimal

solutions between the number of fuzzy rules and the prediction accuracy. However the

experimental results show that the model performs a reduction of fuzzy rules with

similar efficiency with the existing typical data driven fuzzy models.




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A risk-based model is developed by Thodi et al. (2013) scheduling the replacement of

offshore components utilising the possibility of failure and consequences of failure in

terms of cost sourced from time-driven degradation approaches using Bayesian analysis

Yu (2013) develops a Generative Topographic mapping (GTM) and contribution

analysis-based method for turbine engine’s bearings health degradation assessment

utilising Bayesian-Inference-based probability (BIP) for failure likelihood

consideration. A thermodynamic diagnostic approach for Internal Combustion Engines

(I.C.E.) is proposed by Barelli et al. (2013) involving components as filters and

compressor modules simulating the performance degradation considering the effect of

compensation assessing failures using Mamdani fuzzy inference.




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4 CONCLUSION

In summary, this state of the art report illustrated the number of of previous research

works and tools represented that can help INCASS on achieving the most optimum and

innovative maintenance approach. This report has been devided into two major sections

of machinery and structures. However, it can be seen from the report that these sections

are in some extent connected to each other through different types of methodologies that

have been used. Some of the most common maintenance methodologies that are

mentioned in this report are Condition Based Maintenace (CBM), Reliability Centred

Maintenace (RCM), Risk Based Inspection (RBI) and Total Productive Maintenance

(TPM).

Machinery section have demonstrated the major condition monitoring tools such as

Vibrational analysis, Thermography and Lub Oil analysis, which can be evaluated for

selecting appropriate analusis tools for different machinery components of INCASS

consortium. Additionally, it has represented different types of maintenance

methodologies for analyzing condition monitoring results in order to obtain most

optimum maintenance scheduling and analysis system for the INCASS machinery

maintenance planning. Structural section has also provided valuable background on

structural analysis tools and condition monitoring methodologies in order to develop a

unique and efficient structural condition monitoring methodology for the project.

Finally, adding these two major sections together will create excellent foundation for

creating overall INCASS maintenance and inspection system.

Finally, this paper has discussed about all previous relevant research activities (e.g.

RISPECT and MINOAS projects). This also includes methodologies established

previousely by partners specially by classification societies and tools developed by

research bodies such as Reliability and Criticality Based Maintenace (RCBM) by

University of Strathclyde.




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