Condition based maintenance:a surveyAshok Prajapati

FANUC Robotics America Corporation, Oakland, Michigan, USA

James BechtelPM Heavy Brigade Combat Team, US Army,

Warren, Michigan, USA, and

Subramaniam GanesanOakland University, Oakland, Michigan, USA

Abstract

Purpose – The purpose of this paper is to provide a brief overview of condition based maintenance(CBM) with definitions of various terms, overview of some history, recent developments, applications,and research challenges in the CBM domain.Design/methodology/approach – The article presents the insight into various maintenancestrategies and provides their respective merits and demerits in various aspects. It then provides thedetailed discussion of CBM that includes applications of various methodologies and technologies thatare being implemented in the field. Finally, it ends with open challenges in implementing conditionbased maintenance systems.Findings – This paper surveys research articles and describes how CBM can be used to optimizemaintenance strategies and increase the feasibility and practicality of a CBM system.Practical implications – CBM systems are completely practical to implement and applicable tovarious domains including automotive, manufacturing, aviation, medical, etc. This paper presents abrief overview of literature on CBM and an insight into CBM as a maintenance strategy. CBM has wideapplications in automotive, aviation, manufacturing, defense, and other industries. It involves variousdisciplines like data mining, artificial intelligence, and statistics to enable the systems to bemaintenance intelligent. These disciplines help in predicting the future consequences based on the pastand current system conditions. Based on the authors’ studies, implementation of such a system is easyand cost effective because it uses existing subsystems to collect statistical data. On top of that itrequires building a software layer to process the data and to implement the prognosis techniques in theform of algorithms.Social implications – The design of CBM systems highly impact the society in terms ofmaintenance cost (i.e. reduces the maintenance cost of automobiles, safety by providing real timereporting of the fault using prognosis).Originality/value – To the best of the authors’ knowledge, this paper is first of its kind in theliterature which presents several maintenance strategies and provides a number of possible researchdirections listed in open research challenges.

Keywords Condition based maintenance, Maintenance, Data mining, Artificial intelligence, Sensors

Paper type Literature review

I. IntroductionFor many years maintenance ( Jardine and Tsang, 2005) has been an expensive anddaunting element of supporting the product lifecycle of any given system. Variousapproaches have been investigated to minimize failures or correct the historicalfailures of a system (Lorna et al., 2010) – typically, by increasing the frequency ofmaintenance actions. One after another improvement concept has been attempted toovercome the difficulties and inefficiencies faced by the previous generationmaintenance system. Typically, maintenance approaches have been divided into two

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Journal of Quality in MaintenanceEngineeringVol. 18 No. 4, 2012pp. 384-400r Emerald Group Publishing Limited1355-2511DOI 10.1108/13552511211281552

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main categories: preventive and corrective maintenance. This paper discusses a thirdoption, condition-based maintenance (CBM), typically categorized under preventativemaintenance, but may be considered as a new tenant of maintenance for someprofessionals.

The concept of CBM[1] was first introduced by the Rio Grande Railway Company inlate 1940s and initially it was called “predictive maintenance.” The railway companyused CBM techniques to detect coolant, oil, and fuel leaks in the engine by trendingchanges in temperature and pressure readings. The CBM monitoring techniquesserved as a great success in terms of reducing the impacts of unplanned failures andidentifying when to fix a leak or replenish a coolant or oil sump. The US Army caughton to this idea very early and later on adopted it as a key maintenance strategy forsupporting their military equipment.

CBM concepts and applications have emerged in several industries throughout the1950s, 1960s, and early 1970s. Automotive, aerospace, military, and manufacturing arethe main industries where CBM has been embraced and have shown several benefits inboth efficiencies and cost savings. Now very large organizations and companies areinvesting and involved with CBM technology applications including the USDepartment of Defense (army, air force, navy, marines) and companies like GM,Honda, GE, Digitech, Honeywell, and others. Advancements in information technologyhave added accelerated growth in the CBM technology area by enabling networkbandwidth, data collection and retrieval, data analysis, and decision supportcapabilities for large data sets of time series data. The targeted data monitored from avehicle or any system can give deeper insight on system performance, system health,root cause of failures, along with forecasting the remaining useful life of the system ora subsystem. This serves as a huge advantage for sustaining the mission criticalsystems used in aerospace, military, maritime, automotive, manufacturing, and otherindustry domains. These valuable applications and benefits have pushed CBM as a keycapability area to apply to a company’s product line – be it automobiles, planes,weapon systems, or other products requiring regular maintenance. These industriesare focussing on CBM concepts and maintenance strategies by designing CBMtechnology enablers into their current and future system architectures.

As industries move into the future, where machines are unmanned and humanmonitored as closely as they have been in the past, the need for CBM will increase.Robotic systems, unmanned vehicles, windmill systems, manufacturing systems, andoil pumping systems are just a few system examples that could gain many benefits outof CBM concepts and maintenance strategies. Businesses could save significant moneyor improve operational efficiencies if they adopt CBM as a maintenance strategy. It maymean reduction in staff, reduction in supply footprint, cost avoidances on second- andthird-order failure effects, reduction in downtime, and other benefits applicable to theirbusiness domain.

II. Maintenance concepts and strategiesA. Reliability centered maintenance (RCM)RCM[2] enables the formulation of the maintenance strategy by selecting the right mixof corrective maintenance, scheduled-based (or preventative) maintenance, and CBM tofully support the reliability of the system in any given operational environment. RCM isa structured methodology that answers some fundamental questions: Whichmaintenance approach should be applied and to what extent? What are the reasonsfor the maintenance strategy? Is there a way to reduce maintenance costs? Is the

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maintenance strategy supportable (from an operational and cost efficiencystandpoint)[3]. The definition of the RCM given by Moubary (1997):

A process used to determine what must be done to ensure that any physical asset continues todo what its users want it to do in its present operating context.

When designing a system for supportability, one considers RCM in the design phaseand further refines the maintenance strategy throughout the systems engineeringprocess (i.e. validation, verification, and test phases) and into the initial fielding of asystem. System testing and field feedback of system failures assists with therefinement of the RCM model, this feedback loop is truly where CBM technologies canhave the highest impact of improving system performances throughout the lifecycle.It enables a continuous improvement process that companies can leverage to gaina competitive advantage with competitors.

The corrective maintenance (Neelamkavil, 2010) or run-to-failure model is a processor activity that is required to overcome a failure that has occurred or is in the processof occurring. It might constitute repair, restoration or replacement of components, orother things to restore the system to its original state as it was in new condition.

The schedule-based maintenance or preventive maintenance model (Chen andTrivedi, 2001) is a maintenance philosophy based on predefined intervals, no matterwhat the condition of the system or subsystem. For example, oil change in a car isperiodic either based on mileage or time interval, even though significant portion ofoil’s life is still remaining.

The preventive maintenance concept (Neelamkavil, 2010) has been derived froma level of repair analysis to determine the maintenance allocation for a given systemor subsystem. On the other hand, CBM has been derived from RCM[2]. RCM focusseson operating context of the system and deals with functional safety, environmentaland operational failure modes. It seeks to discover the root cause of the failure(s) andperforms the detailed analysis of the reliability of the system components and thesystem as a whole. It is well known that the component/system may survive longerwithout affecting the performance, if it is managed and controlled to stay within thetolerance limits of normal operation.

A strictly preventative or corrective maintenance approach leads to inefficienciesin the use of manpower, downtime of systems, loss of revenue, and otherwastefulness. For example, oil changes in most vehicles are periodic rather thanbased on its quality at a given snapshot in time. The rejected oil could be usedeven more without harming the engine. It may not be the optimal useof resources. In manufacturing plants (Kozusko, 1986), most of the machineries/plants are serviced periodically to avoid unplanned maintenance and downtime.This maintenance strategy leads to periodic downtime for a given plant-thusproducing lulls and inefficiencies in the production process. In a way, it assuressuccessful operation of the plant, on the other hand it affects the plant’s productivityin terms of cost, down time, labor engagement, etc. Such maintenance is popularand is in practice by most industries though it is expensive and can be found to beinefficient.

On the other hand, CBM works on the current condition of the system orsubsystems. The underlying maintenance process eventually triggers a businessprocess (i.e. supply or maintenance action) to mitigate downtime at the optimal time.It implies that a system is utilized as much as it can perform its expected performancelevel. It is then replaced or repaired before it goes below certain performance measures.

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As a result it provides the ability for the system to continue operating as long as it isperforming within predefined performance limits.

The military, manufacturing, food processing, aerospace, automotive, and variousother industries have been considering the use of CBM applications (Neelamkavil,2010; Williams et al., 1994; Barajas and Srinivasa, 2008) to “lean” out their maintenanceprocesses and practices. In many cases, this approach is to be found more useful andefficient than the existing ones. With CBM as part of your maintenance philosophyfocus is put on the current condition of the component/system rather the than schedulebased or run-to-failure type maintenance strategies. Not all subsystems are fit to bemonitored detect impending failures. Thus, depending upon the system or subsystemat hand and the RCM analysis, one must choose the appropriate mix of corrective,preventative, and CBM as part of a system’s maintenance strategy.

B. Equipment failure behaviorUnderstanding of the equipment failure behavior is imperative to developing a CBMstrategy. The well-known curve for the behavior of the failure is called P-F curve[4].Figure 1 represents the P-F curve which shows the fault behavior of the associatedparameter. The underlying assumption is that parameter has for the “higher-is-better”tendency. This curve shows the point where failures start occurring (P) but notdetectable, the point where failure is detectable (P1) is generally known as potentialfailure, and the point where system fails, is called the functional failure (F) point. Thereare various other points between P1 and F, where many algorithms can be developed toidentify these intermediate states, P2-Pn. The time taken from potential failure to decayinto functional failure is P-F interval. All the analyses and maintenance to beperformed are limited to this interval. The order of this interval depends on a givensystem’s characteristics. There are some other probabilistic measures that determinewhen system failures may be detectable, called failure detection threshold (FDT), i.e.the percentage of a component’s life that must elapse before an approaching failure canbe detected. For example, if FDT is 0.7, then fault has probability to occur or beingdetectable only within 30 percent of a system’s life. Since this heavily dependent onsystem characteristics, the FDT varies system-to-system.

C. CBMCBM (Nickerson and Hall, 1995) involves monitoring the condition of missioncritical and safety-critical parts in carrying out maintenance whenever necessary toavoid hazards rather than following a fixed schedule. According to the US Air Force[5],CBM is:

Condition

P P1 P2 P3 Pn

P-F interval

Time

F

Figure 1.Equipment failure

behavior (P-F curve)

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Condition Based Maintenance (CBM) can be defined as a set of maintenance processes andcapabilities derived from real-time assessment of weapon system condition obtained fromembedded sensors and/or external test and measurements using portable equipment. Thegoal of CBM is to perform maintenance only upon evidence of need.

One example of CBM is an application in engine oil quality detection. Some automobilecompanies like GM[3] has deployed a CBM system to detect the oil quality based on thelife of oil components. This system starts notifying operators about the remaininguseful mileage based on the current oil components such viscosity, total acid number(TAN), total base number (TBN), etc. In this case, such a system allows the oil to beutilized to its maximum life rather than replacing oil prematurely at regular intervals.A CBM strategy has been viewed from various angles by academia and a variety ofindustries. The various aspects and enablers of CBM are the focus of later sections ofthis paper.

D. CBMþThe CBMþ includes RCM[2] analysis of the regular CBM components. The US AirForce definition of CBMþ [5] is as follows:

Conditioned Based Maintenance Plus (CBMþ ) expands upon these basic concepts,encompassing other technologies, processes, and procedures that enable improvedmaintenance and logistics practices. These future and existing technologies, processes, andcapabilities will be addressed during the capabilities planning, acquisition, sustainment, anddisposal of a weapon system.

This implies that CBM is not just a “box” you can buy to integrate onto your platformor system, but is a set of integrated technologies, processes, and capabilities thattogether enable CBM to be realized.

III. CBM technology enablersA. DiagnosticsDiagnostics is a process of finding the fault after or in the process of the fault occurringin the system. For example, a vehicle mechanic may try to find a fault code for a faultyvehicle by using a computerized read-out tool and then matching the code with faultdatabase to find corresponding fault information. There are various aspects (Barajasand Srinivasa, 2008) to look into diagnostic process of any system like electricalcomponent diagnostics, vibration analysis, lubrication and oil analysis, infraredthermography, ultrasound, high-speed video and fiber optic, ad hoc system andmachine-level analysis, and other methodologies. For the most part, the systemdiagnostics are a reactive approach to identifying system failures.

B. PrognosticsPrognostics[6] is the process of predicting the future failure of any system by analyzingthe current and previous history of the operating conditions of the system ormonitoring the deviation rate of the operation from the normal conditions. There aretwo major challenges in prognostication:

(1) time to failure prediction or remaining useful life prediction; and

(2) trust value estimation (TVE).

The CBM process is represented by the authors as V diagram shown inFigure 2. The left side of the V shows the methods by which data are collected and

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a database is filled. The heart of the CBM capability is the “expert system analysis”which is the vertex of the V diagram. The right side of the V diagram describes theprognostication and display activities.

Real-time input from the system (various parameters like outside temperature,desired speed, etc.) is fed to a real physical system which produces a set of outputs(actual speed, actual system temperature, etc.). The test inputs are fed to both the realsystem and the system model to check whether the real system works as desired andproduces the correct outputs. The system model produces the expected output for alltest inputs. The outputs of the system model and physical system are collected usingvarious data acquisition methods using sensors, wired and wireless techniques, andstored in a database. The data are compared and analyzed using an expert system. Theintelligent data mining stage extracts the useful data features from the stored historicaldata. Prognostication block applies various algorithms to find the remaining useful lifeof the system components and sends results to display unit and maintenance actionunit. The display unit is also fed by the prognostic modeling unit which verifies theprognosis model with a set of test inputs.

C. Usage-based modelingUsage based models can also be used to predict remaining useful life of components.For instance, General Motor’s oil life prediction algorithm is one such example[3].It is based upon engine revolutions, temperature spikes, and driver usage (hardaccelerations, excessive use of breaks, etc.). These types of usage models requireextensive testing and analysis. The prediction is only as good as the model and there is

Real-timeinput

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Systemmodel

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Intelligentdatabase /data mining

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Prognostication

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Figure 2.The V-architecture of CBM

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a huge margin for error if extensive study is not performed before hand. Thus, modelvalidation and verification testing is of utmost importance when developing anddeploying usage-based models.

D. Data mining in CBMData mining (Hastie et al., 2009) and management (Tsang et al., 2006) techniques areone of the key enablers in the design of a CBM capability. Generally, all systems likemanufacturing, automotives, oil and gas, and others dump large amounts of dataperiodically from different sources. In general data collected from the sensors can behuge and may not be all relevant or meaningful. Data mining helps in extractingmeaningful data and building the model. Model building is finding out the relationshipthat expresses how the change in one or set of variables affect the other variables. Theprocess of data mining generally contains four steps: data preparation, modelgeneration, model validation, and deployment.

The data preparation (Zhang et al., 2003) phase deals with data cleaning, datatransformation, grouping data into smaller groups (i.e. clustering of same type of data),data integration, and data reduction. This stage is the preprocessing stage for the rawdata collected directly from the system under operation or test. Generally, raw datamay be incomplete, i.e. missing/lacking attributes of interest, noisy (i.e. errors orinconsistent that may cause irregularities in the pattern). The preparation stageinvolves various activities (Pyle, 1999) like auditing the data, enhancing and enrichingthe data, finding sampling bias, determining the data structure, building the preparedinformation environment, surveying data, and other data quality steps. This stagetransforms the data from raw to meaningful stage and used to build the model.

Model building is the next stage in the process of data mining and is based on thedata received from the previous stage. In this stage data patterns or features areformed. Trend analysis and multivariable correlation is performed which canprovide the predictive capabilities for the system life or failure projections. A modelis a set of statistical relationships over a period based on the known behavior of thesystem-under-operation or test. Various patterns can be identified for a normal courseof operation, failure states, and additional states between these two in order to findcomplete training sets.

The model validation stage deals with the validation of data patterns created fromthe previous sets. This can be performed using different known sets of data. Themodels should then be validated to ensure they meet the intent of the prognosticoutput.

Finally, the resultant model is used to apply to a new data set in order to predict theremaining system life (i.e. prognostics). The last stage is termed as deployment in datamining terminology. Some popular techniques and algorithm approaches[7]extensively used in data mining are neural networks, decision trees (CART andCHAID), genetic algorithms, nearest neighbor search, rule induction, and severalothers.

E. Artificial intelligence in CBMArtificial intelligence involves the development of powerful reasoning algorithms andprediction techniques (Russell and Norvig, 2002). These algorithms play an importantrole in the prediction of system life and become one of the key components for thesuccess of CBM as reported by Smith et al. (2003). The most prominent applications ofAI techniques are in searching capabilities, for example construction of search plans.

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The focus is to evaluate alternative plans based on their attributes and choose the bestsequence in order to achieve the goal. Similar approaches can be applied to CBMapplications to predict the life of the system based on the real-time attributes collectedfrom the system.

AI also focusses on machine learning algorithms. The goal of these algorithmsis to identify the complex pattern from the sensory data received from the operation orsystem-on-test and then creates the intelligent decisions to make from these data sets.This has one-to-one relation with CBM system because the goal of the CBM system isto make the decision based on the data received from the system throughout the systemlife cycle (Prajapati and Ganesan, 2012).

F. Open system architecture-CBM (OSA-CBM)OSA-CBM (MIMOSA, 2006) is designed by MIMOSA which is an organizationinvolved in the development of the standards for CBM. Several internationalcompanies are members of this organization including the US Army. OSA-CBMis a standard for information flow to help realize an end-to-end CBM system. This hasbeen designed in Unified Modeling Language which fills the gap across differentcommunities for information exchange. OSA-CBM provides the standard frameworkfor the communities, e.g. engineers, scientists, students, and others who are involved inthe development of CBM systems. It has six building blocks: advisory generation,prognosis assessment, health assessment, state detection, data manipulation, and dataacquisition. At the end advisory state tells the action to be taken. It has variousadvantages and important features (e.g. cost, specialization, competition, co-operation,etc.). Prognosis stage deals with the prediction of future failure that are likely to happenand the remaining useful life of the system. Health assessment specifically looks for thefaults or failures about to happen. As name suggests, state detection stage finds outthe state of the device based on the information received from the previous stage.Data manipulation stage is responsible for the analysis of the data gathered from thedevice-on-test. Data acquisition deals with the conversion of data into human readableformat. Table I summarizes the level of capabilities for each maintenance approachin brief. Each maintenance technique has its own limitation and can be chosen basedon the application requirement. CBM is most efficient among these for long-termmaintenance requirements. Initial infrastructure deployment is expensive incomparison, but it provides several benefits over the lifecycle of a system. It is wiseto establish such maintenance concepts for maintenance of plants, food processingindustries, fleet management, and other applications. CBM is useful for all thoseindustries which have systems to support from maintenance and supply perspective(i.e. fleets, plants, weapon systems, and others).

IV. Survey of recent developments in CBMVarious research communities are actively contributing in different ways to matureCBM capabilities. The US Air Force is highly involved in developing CBMþ [5]applications for the Joint Strike Fighter. They are also planning to take CBMþinto consideration while designing and developing new weapon systems. “ConditionBased Maintenance Plus (CBMþ ) is the application and integration of processes,technologies, and knowledge-based capabilities to improve the reliability andmaintenance effectiveness of Department of Defense systems and components.”CBMþ uses a systems engineering approach to collect data, enable analysis,and support the decision-making processes for system acquisition, sustainment, and

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operations. Using a CBMþ strategy is found to be more efficient and helps savemoney across the lifecycle of a given system.

Maintenance strategies have always been a point of concern and costly portion ofany degradable system. In the past, various attempts have been made to improvemaintenance processes. Recently the focus has been shifted toward CBM due to itspredictive nature and positive impacts on the supply chain and fleet management.CBM intelligence is centered on the prediction algorithms used for fault prognosis.In the following section, we have addressed some existing efforts toward improvementof fault prediction and CBM.

Yang (2002) examined the Kalman filter (KF) for one-step-ahead prediction in DCmotor maintenance. KFs are computationally efficient especially for the applicationshaving large states. Mr Yang reports the error estimated in one-step prediction isacceptable. While it is not acceptable for highly critical systems, it is complicated andcomputationally expensive.

Grall et al. (2002) proposed a very nice and general approach applicable to anydegradable system. But they restricted the approach with certain limitations anddependencies which does not seem to be very practical in real life applications.

Ferrell (2000) presented a strategy for the detection of the abnormal behavior of thesystem based on the analysis of normal behavior. They proposed a general supportvector representation of a machine for one classification of a non-stationary classas well as additional stationary classes. It deals with data even if some classes aremissing by creating new classes. This method helps to better predict the future healthof the system.

Lu et al. (2007) investigated a predictive CBM capability to predict a deterioratingsystem’s future condition. The degradation states are modeled as continuous states andfault probability is dependent on random variables. The proposed strategy is centered onthe maintenance cost, while prediction accuracy becomes the most important factor.Specifically when CBM is applied for critical systems it is primarily to detect andpredict the fault conditions within very small intervals. According to the authors,a good maintenance system has to have good balance between prediction accuracy andmaintenance cost.

Furthermore, Rausch (2008) has investigated a CBM methodology that establishesthe relationship between continuous state/time degradation model and CBM systems.This approach is not dynamic as he mentioned in his future research. This has been acenter of attention in our proposed system.

Chen (Chen and Trivedi, 2002) presents a closed form analysis for the systemwhen it deviates from the normal course of operation and meets poison’s failures.Authors investigate a closed-loop solution to optimize inspection intervals to maximizethe system availability, minimize the operational cost, or mean time to failure undercertain system availability constraints. The uptime of the system using a polynomialequation-based solution is found to be deterministic, faster, and suitable for real lifeapplications.

Yam et al. (2001) have developed an intelligent decision support system for CBM.This system is based on a recurrent neural network that adds capability parameters topredict future fault conditions. The neural network approaches are efficient, butrelatively slow. These approaches also require large set of data for training whichturns out to be a big restriction for a real-time application.

Angel et al. (2000) has presented a nice method for failure prediction by focussing onaccuracy, precision, and confidence. In short their results show that

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5 percent deviation in expected failures, which is still high for critical or real timeapplications.

Baruah et al. (2006) have presented a generic framework for diagnostics andprognostics. They have designed an online approach which does not requireinteraction with the physical system, and in turn, reduces manpower. This frameworkis based on multi-basis clustering as well as optimized cluster tracking. It has somedrawbacks due to some of the subjectivity bias caused by human decision inputs.Kwon (Kwon et al., 2009) has also proposed a similar methodology as in (Baruah et al.,2006) because both of them discussed the online maintenance approach. This paperfocusses on the analysis of data available through internet for the remotely locatedsystems. Mainly the robot’s harmonic drive is connected to the web and all the real-time data are available to a remote off-line analyzer. A mathematical model has beendeveloped to predict the failure of any subsystem of the robot. The experimentalresults show that this strategy is more helpful to increase the system’s uptime. Thevarious optimization methods have also been proposed to optimize CBM enabledsystems in practice.

Chen and Trivedi (2005) used semi-Markov’s decision process (SMDP) foroptimization of a CBM policy. Chen has proved that optimization over inspection rateas well as maintenance policy is better than that of only over inspection rates. SMDP iswell-established approach and good for modeling numerous failure scenarios. Onthe other hand, it requires a large data set for training and it is not well suited fortime-dependent degradations. As a result, it makes SMDP as not very useful for CBMapplications, especially for highly critical and time varying types of failures.

Schmitigal and Moyer (2005), discussed an oil condition monitoring system fordiesel engines. In this research project, they tested mainly two major issues: sootaccumulation and oil oxidation using an on-board sensor system. The results from theon-board sensor systems and the results of on-site analysis were compared to check theaccuracy of the results from the on-board sensor system. Results found that thisparticular on-board sensor system could be utilized to predict the oil quality directlyfrom the fleet without the help of an oil analysis lab.

In the paper, Sondalini proposes that 15-20 percent of equipment failures are due toaging/degrading factors and the remaining (80-85 percent) are random failures. Therandom failures can be predicted only by analyzing failure patterns or using somefailure prediction models. Schedule-based maintenance is useless for suchunpredictable failures while some factors other than aging/degrading factors arealso responsible for failures.

The paper authored by Zachos and DeGrant (2009) discusses the enhancement of asmart wireless internal combustion engine (SWICE) device that has been treated as amini-vehicle computer system. The SWICE includes a smart wireless diagnostic sensor(SWDS) which enables reach in to the vehicle subsystems through a wireless link froman at-platform device such as a ruggedized laptop. SWDS helps in establishing anetwork on an ad hoc basis with an off-platform fleet data collection terminal. Someusage data like miles, hours, faults, temperature, pressure, and other vehicleparameters can be downloaded from the wireless interface of the device and used todirectly analyze the vehicle data.

V. Application areas of CBMCBM has wide variety of applications in manufacturing plants, process industry,military, naval, air force’s ground vehicles, IT infrastructure, commercial vehicles, and

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aviation/aircraft. We provided the brief direction on engine oil, vibration analysis, andtemperature analysis for IT infrastructure. These are the few applications areas forCBM. Manufacturing is a highly targeted domain where CBM is being applied.

A. AutomobilesIn this section we study the components for the engine oil [3] used in vehicleengines of army trucks, commercial vehicles, or other engines utilizing engine oil.There is a general practice of changing oil every 3,000 or 7,500 miles or other intervalin normal service condition depending upon the manufacturer. As it is discussed inearlier sections, oil quality might not have gone below the required threshold. It mightserve the engine next several hundreds of miles without affecting the performance.Currently, some advanced automobiles are fully equipped with oil condition sensorsthat notify the operator to change oil only when the oil’s quality goes below the desiredlevel rather than changing the oil in a prescribed time interval.

There are various reasons behind oil degradation with time. Mainly, engine oil getssome contaminants during engine operation. It can be analyzed by taking somesamples from the engine. These materials include dust, fuel, or antifreeze, etc., thatmake the engine oil quality even worse than it should be in normal course of operation.Following are some examples of contaminants and their impacts:

. silicon-dust and fine dirt from a leaking air system;

. chromium-piston ring wear;

. copper, tin-bearing wear, sodium-coolant leak; and

. iron-liner, crankshaft, camshaft, cylinder liner, and timing gear wear.

The quality of engine oil (Schmitigal and Moyer, 2005) depends on the five maincomponents of the oil as listed:

. viscosity index (VII);

. TAN;

. TBN;

. Oxidation induction time; and

. dielectric strength

The viscosity of oil is the measure of thickness of the oil; lower the viscosity thinner theoil and flows more easily. On the other hand, thick oils are more resistant to flowbecause of thickness (i.e. highly viscose). These oil grades are standardized by SAEand represented by xWy, where x represents the pour point of the oil in degreecentigrade, “W” stands for the winter grade and the number followed by is oil weight,i.e. VII at 100 degree centigrade. Few of available engine oils in the market are 0W20,5W30, 10W30, etc., which are multi-grade oils suitable for low as well as hightemperatures. The viscosity is one of the key components to determine the condition ofthe used engine oil. Single grade oils do not carry VII.

TAN is another important factor in the quality measure of engine oil. This numberis the acidity index of the oil deals with all acidic materials available in the oil. As oilquality decreases this number goes up because acidic components (i.e. rust, corrosion,dust, etc.) increase with the use of oil. Typically TAN numbers vary between two andfour depending upon the oil.

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The TBN number deals with alkaline components of the oil which are responsiblefor neutralizing the acidity of the engine oil. This number typically ranges betweenfive and 15. The TBN number drops with use of oil because more acidic componentsare infiltrating into it the oil. When this number goes below three it is no more usefulfor quality lubrication. After the brief study of some oil quality parameter, theappropriate oil change interval can be determined which results in the best utilizationof engine oil.

B. IT infrastructureMaintenance is one of the key issues in an IT infrastructure. The informationtechnology revolution has made an IT infrastructure as much important as machinesin the manufacturing plants. Nowadays all the important data are being stored inelectronic form on hard disks. Global businesses are utilizing large data centers to storethe electronic data. A single point of failure in servers may lead to the largest lossfor any big enterprise. These issues provoke the big questions of how to providesafety and maintenance of infrastructures to provide quality of service and reduce therisk of losing data. Many other factors cause disk failures. There are four well-knownparameters, called SMART parameters:

. reallocation counts;

. probation counts;

. offline reallocation; and

. scan errors.

The study in Pinheiro et al. (2007) shows that after the first reallocation, disk driveshave 14 percent more chances of failure than that of ones without such errors. Thisparameter has critical threshold value of one. The disk drives have 16 percent higherprobability after first probation count than normal disk drives. The critical thresholdfor probation count is also one. Similarly, after initial offline reallocation disk drives are21 percent more likely to be failed than normal disk drives. The disk drives with firstscan error have probability of 39 percent than that of without scan error failure. All theabove parameters have been studied in a 60-day period. The prediction models basedon these SMART parameters may not give the desired behavior because disk failuresare based not only on SMART parameters but also on some other signals like seekerrors, CRC errors, power cycles, calibration retries, spin retries, power-on hours, andvibrations. The overall study investigates that approximately 56 percent failures arefound without any error in SMART parameters, namely scan errors, reallocation count,offline reallocation, and probation count. Even though all parameters are added excepttemperature in failure analysis, 36 percent failures reflected were due to other signals.

C. Process/manufacturing industryThe maintenance of production plants is generally done when a failure has occurred.The defective unit might be refurbished or replaced based on the condition of thedefective unit. These unexpected failures could cause significant down time ofthe plant. Vibration sensors offer a vast variety of services in failure analysis. Thevibration sensor can be mounted on any critical part such as motors, fans, gearboxes,pumps and vibratory conveyors, bearings, and other rotating mediums. CBM is analternative approach that significantly improves the down time by predicting thefailure ahead of time based on vibration analysis. It has the capability to predict the

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remaining system life. Over a period of time deterioration increases andvibration pattern changes, this can be easily detected through an FFT analysisof the vibration of a given component of the system. An adaptive model ofvibration analysis is well presented by Zhan et al. (2003). An approach of optimizingCBM decisions based on vibration monitoring is discussed by Jardine ( Jardine et al.,1999).

Vibration analysis of the cooling tower at PCS Potash New Brunswick Division thatis a potash and salt mine near Sussex, New Brunswick has been discussed in Copp andPieroway (2009). The cooling tower is considered to be one of the critical pieces ofmachinery in the plant. These subsystems save many other machineries frommalfunctioning or failing. The vibration sensors collect data continuously from thegearbox and sending to analyzer that does the pattern analysis and detects theanomalies in the system based on frequency levels.

VI. Open research challengesCBM is a very useful and powerful maintenance approach, but on the other hand theinitial costs of deployment can be fairly expensive. Even though initial deployment isexpensive, it can be very effective to meet long-term maintenance and supportabilitygoals. Next generation systems are highly equipped with sensors for various purposes.It is easier to build a CBM application with minimal changes within an existinginfrastructure. Some specific components need to be monitored as well as datatransferred over wired or wireless networks to maintenance management systems andenterprise resource planning systems. There are also some other factors to beconsidered in the planning process. Below is a list of several but not all open researchchallenges.

There are various concepts outlining different CBM architectures. Different researchcommunities are following different architectures that stovepipe many of the systemdesigns. It also leads to lack of interoperability and promotes proprietary solutions.A standard and scalable CBM architecture is needed to maintain interoperability.Interoperability seeks to assist with integrating components from many vendors tobuild efficient CBM applications and meet the specific requirements for customers.

A. Real-time prognosticsFault prognosis is the core of CBM application development. It is a big challenge to findor develop an algorithm to predict the fault conditions based on available data. Thereare various aspects to consider in algorithm selection or development. How effectivelyfault can be predicted (prediction accuracy)? How long algorithm takes to computeprediction samples once data is available (response time)? How much input data isrequired (input size)? Are all these parameters in the desired range (real-timeapplication requirement)? How frequently prediction model needed to be built? Allthese parameters in turn decide the CBM application efficiency.

B. Data quality: preparation/selectionData preparation and/or right data selection is another factor to be considered.Especially in multi-sensory system, there is huge database available. Choosing someout of million samples is another challenge for the designer. These some questions tobe answered: How much data needs to be selected? What is the criterion to select data?Is this criterion robust? Is this data representative of fault conditions? How long data isneeded to be stored? How old is the data a model is relying on?

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C. Data collection/transmissionAdvances in electronics supported automation industries in wide scale. Sensory datacollection is handled with wired or wireless medium. Nowadays CAN bus is verypopular for industrial networks and widely used in automotive environment. Ethernet-based communication is pretty popular and cheaper. Some industries are alsomigrating toward wireless communication. Wireless communication has its ownadvantages over wired communication. Recent developments in wireless technologyprovide routing traffic over existing infrastructure, easy maintenance, availabilityof desired communication at each corner, maintenance information on the go, datamonitoring while on move, etc. Now the challenge is efficient design of communicationprotocols to serve this purpose. A few things to consider: How reliable is yourcommunication infrastructure? How secure is your communication infrastructure?Is data transmission real-time? Is it capable of handling high-bandwidth traffic? Doestransmission time meet application requirement? Is throughput enough? Does packetlatency meet real time application requirements? The development of suitable sensors(associated communication protocols) with low power consumption, smaller size, andhigh accuracy are required for various real-time applications.

VII. ConclusionMaintenance has always been an integral part of any system support plan. Most of ushave been following recommended maintenance procedures without giving muchthought about actual system condition and remaining useful life of removed parts.We have been performing it to avoid sudden failures or because the maintenancemanual tells us to do it as part of a schedule. This has led to vast waste in terms oftime and money. In this paper, we have turned the reader’s attention toward CBMconcepts which has been proven to be more efficient than traditional maintenanceapproaches in various respects. The brief review of literature provides how activelyresearchers are engaged in the advancements in technology that enable CBMcapabilities. This paper also provides an overview of some of the key enablers forCBM like diagnostics, prognostics, data mining, and artificial intelligence. Finally, thepaper concludes with a few open research challenges to improve CBM enablingtechnologies.

Notes

1. History of CBM (www.omdec.com/moxie/Technical/Reliability/a-history-of-cbm.shtml)(accessed June 8, 2010).

2. RCM (www.mutualconsultants.co.uk/rcm.html) (accessed January 12, 2010).

3. GM oil life prediction (www.gm.com/corporate/responsibility/environment/maintenance/simplified_maintenance_040104.jsp) (accessed March 28, 2010).

4. The Reliability HotWire (eMagazine), Issue 76, June 2007 (www.weibull.com/hotwire/issue72/relbasics72.htm) (accessed July 21, 2010).

5. CBM and CBMþ (https://acc.dau.mil/GetAttachment.aspx?id¼32779&pname¼file&aid¼6236&lang¼en-US) (accessed March 28, 2010).

6. Advanced amphibious assault vehicle prognostics/diagnostics overview, 2006 (www.dtic.mil/ndia/2006electronic_prognostic/efv.pdf) (accessed April 20, 2010).

7. Data mining (www.thearling.com/text/dmwhite/dmwhite.htm) (accessed September 28,2010).

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Corresponding authorAshok Prajapati can be contacted at: ashok.prajapati@fanucrobotics.com

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