full scale data handling in shipping: a big data solution

Norsk Marinteknisk Forskningsinstitutt

Lokukaluge Prasad PereraNorwegian Marine Technology Research Institute (MARINTEK), Trondheim, Norway.

March 2016.

Trondheim, Norway.

Full Scale Data Handling in Shipping: A Big Data Solution.

•Introduction

•Objectives

•Data Analytics & Sensors

•Energy Flow Path: Marine Engine Centered Approach

•Data Flow Path: Big Data Challenges− Sensor Fault Identification

− Data Classification

− Data Compression

− Data Expansion

− Data Integrity

− Data Regression

•Conclusion & Future Activities

Outline

Data Analytics

Data Management

Introduction

•Big Data Solutions play an important role in Future Research and Industrial Applications.

•Strategic Priority Area for the MARINTEK.

•Research and Industrial Applications:− Data Management: Appropriate actions to develop a bunch of data in a

structured collection.

− Data Analytics: The science of examining these data with the purpose of drawing meanings about the information.

•The size of these data sets may not make a big difference in these applications.

•The outcome of the Big Data, the meaning, is the most important aspect of these research and industrial applications.

•Many Fundamental Challenges.

Objectives

•To address the Fundamental Challenges in Big Data Applications in Shipping.− Large scale Data Sources => Data management

− Sensor Related Issues

− Quality of the data

− Data communication

− Data Interpretation => Data Analytics− Energy Efficiency

− Reliability

" The data has a structure and

the structure has a meaning"

Data Analytics & Sensors•The main focus point

•Empirical/Stochastic Models− Various Empirical/Stochastic Models have been developed in shipping.

− Some challenges in handling Big Data.

•Machine Intelligence− Machine Intelligence (MI) can play an important role in the outcome of Big

Data applications.

− MI Techniques are extensively implemented on current Big Data applications.

− These tools and techniques and their applicability in shipping should be investigated.

•Knowledge on the Vessel:− Ship Dynamics/Hydrodynamics

− Automation and Navigation Systems

− Localized Models in Ship Performance Monitoring

Energy Flow Path

•The possible situations of energy conservation:− Marine power plant.

− Engine propeller interaction.

− Ship resistance.

Marine Engine Centered Approach

Localized Models in Ship Performance Monitoring

Engine Propeller Combinator Diagram


Possible Region of Engine-Propeller Operations

Basis for Localized Models in Ship Performance Monitoring

Vessel Information

•The respective data set of ship performance and navigation information is collected from:

•Bulk carrier with following particulars: − ship length: 225 (m),

− beam: 32.29 (m),

− Gross tonnage: 38.889 (tons),

− deadweight at max draft: 72.562 (tons).

− Powered by 2 stroke ME with maximum continuous rating (MCR) of 7564 (kW) at the shaft rotational speed of 105 (rpm).

− Fixed pitch propeller diameter 6.20 (m) with 4 blades

Statistical distributions of Engine Speed, Power & Fuel Consumption

Engine Operating Regions: Engine Power vs. Shaft Speed

Engine Operating Region vs. Rel. Wind Speed, Avg. Draft, Trim and STW


Operating Patterns

Engine propeller combinator diagram with STW

High to Low STW

Data Flow Path

Data Classification

•Engine Centered Data Flow Path

•Localized Models in Ship Performance Monitoring

•Algorithm− Multivariate Gaussian distribution

− Gaussian Mixture Models (GMMs)

− Expectation Maximization (EM) algorithm

[Source: Matlab.com]

Data Classification: Engine Propeller Combinator Diagram

Localized Models

Principal

Component

Analysis

(PCA)

Model 3

Sensor Fault Detection

Considering

a Two Sensor Situation

Fault Level 1

Considering 10 Parameter Situation

Parameter Mini. Max.

1. Avg. draft (m) 0 15

2. STW (Knots) 3 20

3. ME power (kW) 1000 8000

4. Shaft speed (rpm) 20 120

5. ME fuel cons. (Tons/day) 1 40

6. SOG (Knots) 0 20

7. Trim (m) -2 6

8. Rel. wind speed (m/s) 0 25

9. Rel. wind direction (deg) 2 360

10. Aux. fuel cons. (Tons/day) 0 8

Fault Level 1

Considering a Two Sensor Situation

e3

e1

Fault Level 2Principal Component Analysis (PCA)

Data Standardization.

Mean = 0

Variance = 1

Least Principal Components

Projected Data into PCs

e1, e2, e3, & e4

Data Distributions in PC Axes

Least Principal Components

Real-time FaultDetection

Data Flow Path

Autoencoder : Deep Learning Approach

•Encoder Side: Data Compression

•Communication Network

•Decoder Side: Data Expansion

•Top Principal Components

Autoencoder : A Deep Learning Approach

Singular Values Data Compression Information

•Top 10, 9, 8 and 7 principal components can preserve 100 %, 99.92%, 99.48%, 97.86% 94.03% of the actual ship performance and navigation information.

•The respective 99% and 95% lines are also presented in the same figure.

•Top 7 PCs Selected

•10 Parameters => 7 Parameters

•Preserve approximately 94% of the actual information.

PCA of Ship Performance and Navigation Information.

PCs for Ship Performance Evaluation ?

PCA of Ship Performance and Navigation Information.

Actual and estimated parameters of ship performance and navigation information.

Actual and estimated parameters of ship performance and navigation information.

Data can be Recovered by Regression or Smoothing

Data Flow Path

Data can be Recovered by

Regression or Smoothing

AIS Data

Data Analysis

Relative Wind Profile of a Ship

Relative Wind Profile with STW and SOG

Gaussian Type Distributions

Wind Sensor Faults

One Sided Relative Wind Profile (Cleaned Data)

One Sided Relative Wind Profile with STW (> 3 Knots)

One Sided Relative Wind Profile (Further Cleaned Data)

Ship Speeds: STW, SOG and STW-SOG

Ship Speed Power Profile with Relative Wind Speed|STW –SOG| < 5.5 (Knots)

Speed Power Profile with Rel. Wind Speed and Angle

STW-SOG vs. Rel. Wind Speed and Direction

Modified Speed Power Profile

SWT vs. SOGUnique to the Vessel ?

Ship Performance Data

Conclusion & Future Activities

•Some advanced tools are developed in this stage.•Still a Logway to go..

− Sensor Fault Identification− Data Classification− Data Compression− Data Expansion− Data Integrity− Data Regression

•Models should be further developed.•High sampling rate data •Further collaboration with appropriate partners.•Research projects.

Thank You

Questions ?This work has been conducted under the project of "SFI Smart Maritime - Norwegian Centre for improved energy-efficiency and reduced emissions from the maritime sector" that is partly funded by the Research Council of Norway.

smartmaritime.no

Publications and high resolution color images: http://bit.do/perera.

full scale data handling in shipping: a big data solution

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