Integrated Diagnostics and Prognostics Rotating Machinery

International Journal of Rotating Machinery1999, Vol. 5, No. 1, pp. 35-40

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Integrated Diagnostics and Prognostics ofRotating Machinery

KAM W. NG*

Program ofjcer, Office of Naval Research, 0NR333, Mechanics & Energy Conversion Division,800 North Quincy Street, Arlington, VA 22217-5600, USA

(Received 9 April 1998," In finalform 10 May 1998)

This paper provides an overview of current research efforts in integrated diagnostics andprognostics of rotating machinery. Specifically, the following topics are discussed: sensingtechniques and sensors; signal detection, identification and extraction; classification offaults; predictive failure models; data]model fusion; information management; and man-machine interface. Technical issues, recommendations, and future research directions arealso addressed.

Keywords." Condition-based maintenance, Diagnostics, Prognostics, Sensor,Detection and classification, Data fusion

INTRODUCTION

In product manufacturing and power generationoperations, downtime and the associated costs ofmachinery breakdown can be significant and, attimes, even crucial to continued viability. Formilitary platforms and systems, failure ofmachinery can significantly reduce mission effec-tiveness, and could indirectly result in casualty andloss of national assets. As equipment ages, main-tenance costs increase while reliability and safetymargins decrease. Aging equipment, reducedpersonnel support, and a continuing emphasis onreduced operating budgets have combined to kindle

great interest in machinery fault diagnosis andprognosis. The attention given to machinery diag-nostics and prognostics is reflected in the largenumber of references in the literature, see Lyons(1987), Dimentberg et al. (1991), Cempel (1991),Natke and Cempel (1996), and the Proceedingsfrom the MFPT meetings (1968-1997).

Rotating machinery is a major and criticalcomponent of many mechanical systems found inindustrial plants, as well as in air and groundtransportation vehicles and many other applica-tions. The machinery’s rotating elements haveunique characteristics both in performance andacoustics that can be predicted. For example,

*Tel.: (703)696-0812. Fax: (703)696-2558. E-mail: ngk(a;onr.navy.mil.

35

36 K.W. NG

these rotating elements exhibit high harmonicoscillations when on the verge of failure. Thus,rotating machinery is an application where faultdiagnosis is commonly employed as a maintenancemethodology. This paper focuses on the mainte-nance diagnostics and prognostics of rotatingmachinery.

Recently, maintenance methodology has beenshifted toward Condition-Based Maintenance

(CBM). CBM employs a continuous maintenancemethodology to maximize availability of equip-ment. It is accomplished by continuously moni-toring equipment conditions and performingmaintenance actions only when faults exist. At theoperational level, CBM alerts operators to evolvingfaults and allows maintenance actions to be takenbefore catastrophic failure occurs.A schematic of a typical CBM system is shown in

Fig. 1. The key components consist of machinery,sensors, fault detectors, fault classifiers, predictivemodels, model/data fusion, and outputs. In thefollowing sections, technical issues and the func-tions of each component and their interfaces, aswell as on-going research efforts, are discussed.

Technical Issues

Some ofthe issues relating to machinery diagnosticsand prognostics are: (1) database, (2) real-time and

on-line monitoring capabilities, (3) extraction offeatures from noisy environments, (4) errors anduncertainties in detection and classification lead-ing to false alarm, and (5) identification of thedominant modes of failure.A comprehensive database of machinery moni-

toring signals covering normal operation to failureis lacking, principally because legal issues restrictthe dissemination of machinery failure data. Tocircumvent this difficulty, artificial or simulated

(seeded) faults are introduced to speed up themachinery degradation process.From a fault detection and classification view-

point, it is ideal to have real-time and on-linecapabilities. However, it is an on-going challengeto develop such data acquisition and signal analysessystems. To deal with background noise contam-ination and interference, signal enhancement or

noise removal techniques are applied to monitoredsignals. Errors and uncertainties in detection andclassification of fault features can lead to falsealarms. This issue becomes the motivation fordeveloping a robust and reliable machinery moni-

toring system.In failure analysis, identification of the failure

mode is one of the key objectives. It is also the first

step to an understanding of how a mechanicalcomponent fails. This understanding can then beused to develop a physics-based model.

Machinery

Sensors

FaultDetectors

PredictiveModels

FaultClassifiers

Model DataFusion

Outputs:

WarningDetectionClassificationLife Prediction

FIGURE Key elements of condition-based maintenance system.

INTEGRATED DIAGNOSTICS 37

SENSING TECHNIQUES AND SENSORS

Monitoring Parameters

The monitoring of machinery performance and itsoperating condition can be accomplished throughcareful selection ofmeasured parameters. The mostcommon parameters are vibration and acousticsignatures, temperature, pressure, motor current,wear debris and electrostatic exhaust measure-ments. To detect failure inception, deteriorationmechanisms are continuously monitored and initi-ation of cracks, fractures, and other failures, suchas shaft cracks, seal leakage, and corrosions areidentified.

Sensing Techniques

Sensing techniques have undergone continuousdevelopment. State-of-the-art techniques include:active interrogation approaches, nondestructiveevaluations such as acoustic emission and stresswave monitoring, holographic imaging, oil debrisanalysis, chemical composition and analysis, use ofin situ and embedded microsensors, electrostaticexhaust measurements, remote sensing, and elec,

trical resistance measurements. Also importantare the optimal placement of sensors and a sensingstrategy that monitors small changes in statevariables that could capture fault precursorsand features. Virtual sensor concept, a simulationand modeling of signals based on limited data andmeasurements, has good potential to reduce thenumber of sensors and thus cost.

Sensors

Recent advances in materials and sensor develop-ment, combined with newmanufacturing techniques(e.g., nanofabrication) have allowed manufacturersto build rugged sensors that are affordable andminiature in size. The new materials and processinginclude piezoelectric elements, piezofilm, piezocom-posites, optical fibers, and micro-electromechanicalsystems (MEMS). In addition, biosensors are beingdeveloped to sense and identify chemical species,

which will have good potential for use in faultdiagnostics.

FEATURE EXTRACTION

Detection and Identification

With the increasing need for identification andlocation of incipient failures, new signal processingtechniques have been implemented to enhancedetection and identification of fault precursorsand features. Specifically, signal enhancement or

background noise removal, and signal detectiontechniques are used. The signal processing tech-niques include cepstral analysis, time-frequencyanalysis, nonlinear dynamic techniques, and higherand lower order spectral analyses. Time-frequencyanalyses such as the Wigner distribution andwavelet transforms have been used and success-fully demonstrated to detect and identify faultfeatures. Sources of higher harmonic oscillationsin rotating machinery have been found to be causedby imbalance, misalignment, and nonlinear excita-tion. It has been observed that nonlinear dynamictechniques and lower and higher order spectral(polyspectral) analyses techniques can be used todetect and identify higher harmonic oscillations,as well as fault features. In particular, alpha-stabledistributions, a lower-order statistics method, hasbeen developed to detect impulsive type featuresembedded in non-Gaussian signal.

Feature Classification

Once fault features are detected, identified andextracted using signal processing techniques, thefault features are transformed to fault vector

space where classification is performed. Classifica-tion techniques include conventional statisticalmethods, neural networks, fuzzy logic, and patternrecognition. Neural networks have been commonlyapplied to fault vectors to train and classify thevarious faults. Fuzzy logic, a rule-based reasoningapproach, has been used successfully to classifyfaults. Efforts are on-going to combine neuralnetworks and fuzzy logic, the so-called neuro-fuzzy

38 K.W. NG

network approach, to classify fault precursors andfeatures. Pattern recognition still remains as one ofthe classification techniques for faults.

PREDICTIVE MODEL

Failure analysis, which plays an important role inunderstanding how components fail, forms thebasis of the failure predictive models. As mentionedpreviously, failure mode identification and classifi-cation is one of the technical issues. Though failuremechanisms have been studied for a long time,understanding how failures occur and theoreticaldevelopment in predicting mechanical failures arefar from satisfactory. In particular, there is a needfor physics-based models relating fundamentalswith experimental data to predict the remaininguseful (safe operating) life of structures andcomponents. The challenge is to model faultinitiation and propagation as a unified approachand then predict state variables that relates tomechanical response or vibration. A statisticalapproach has been used to predict the fatigue lifeexpectancy of structural components subject tocyclic loading. This approach deals with thestatistical variability in such quantities as materialproperties and initial flaw sizes, as well as thestatistical nature of the fatigue process.

DATA/MODEL FUSION

Data monitoring and predictive models need to befused to enhance decision-making and to providereliable information and warning to operators.Occasionally, signals from multiple sensor inputsand models can be contradictory; therefore, selec-tion of the proper data and integration or fusion ofthis information are critical in the reasoning anddecision-making processes.

INFORMATION MANAGEMENT ANDMAN-MACHINE INTERFACE

The purpose of any diagnostic and prognosticsystem is to provide early failure warning to the

operators. More important, such a system predictsuseful life after a fault precursor is detected. Inaddition to providing reliable information to theoperators, the outputs from the diagnostic andprognostic system must be simple enough that theoperators can understand them and thus takeappropriate corrective actions.An information system workstation is required

to integrate data monitoring, predictive models,and decision support systems for maintenanceoperators in carrying out troubleshooting, man-machine interface, and operator training.

CURRENT RESEARCH ACTIVITIES

In view of the requirements and opportunities inscience and technology, both government andindustry have engaged in a wide spectrum ofresearch and development efforts in CBM. Forexample, the Office of Naval Research (ONR) hasembarked on an integrated science and technologyprogram in CBM. A listing of current researchactivities in CBM is summarized below. Asshown, the research and development efforts covera wide range of projects in basic and appliedresearch, as well as advanced engineering develop-ment in the areas of sensing techniques and sen-

sors, fault detection and classification, predictivemodels and information management, and main-tenance system development.

Current CBM Activities at ONR

(1) Multidisciplinary University Research Initia-

tive (Basic Research)(a) Penn State University/Rensselaer Poly-

technic Institute-Sensor, Fault Detection & Classifica-

tion, Active Methodology, Data Fusion

(b) Georgia Institute of Tech./Northwestern/University of Minnesota

Microsensor, Fault Detection & Classi-fication, Failure Mechanisms & LifeEstimation

INTEGRATED DIAGNOSTICS 39

(2) Future Navy Capabilities Option (Basic &Applied Research & Advanced Development)(a) Machinery

Penn State University: Gas TurbineGeneratorHoneywell Team: CentrifugalCompressor

(b) Oil Debris AnalysisNaval Research Laboratory:LASERNET

(c) CorrosionNaval Air Warfare Center: AircraftCorrosionNaval Surface Warfare Center: Valves

(d) Information Management and Training(3) Surface Ship Technology (Applied Research)

Naval Surface Warfare Center, Annapolis:Diagnostics of Electrical Components

(4) Logistics (Applied Research)Naval Surface Warfare Center, Annapolis:Diagnostics of Mechanical Components

(5) DARPA Flexible Fabrication ProgramUniversity of Arkansas: Portable holographictechnique for monitoring stress and strain instructures

(6) DARPA Technology Reinvestment ProgramModel Based Control Diagnostics: GEAircraft Engine, GE R&D, Lockheed Martin,Ford: Aircraft Engines and AutomobileEngines

(7) SBIR Phase IIBarton Associates: Prognostics of Gear

Specifically, two parallel and complementaryresearch initiatives in CBM have been conductedby the Penn State University, and Georgia Instituteof Technology teams. A Future Naval CapabilitiesOption is underway by the partnership of univer-sities and industry in the focus areas of machinery,oil debris analysis, corrosion of piping andvalves, and information management and train-ing. In particular, the maintenance of gas turbine

generator is addressed by the Penn State Univer-sity team, and the centrifugal compressor problemis being considered by the Honeywell team. Diag-

nostics of rotating machinery and electricalcomponents is being investigated at the NavalSurface Warfare Center (NSWC) in Annapolis,Maryland. In the Flexible Fabrication Programunder sponsorship of the Defense AdvancedResearch Agency (DARPA), the University ofArkansas is developing a portable holographictechnique for monitoring stress and strain instructures. In the DARPA’s Technology Rein-vestment Program, the GE Aircraft Engine Consor-tium is developing model-based control diagnosticsystems for aircraft engines and automobile engi-nes. Under a Small Business Innovative Research(SBIR) Phase II effort, Barron Associates is

developing prognostic capability for the gearcomponent.

CONCLUSIONS

Integrated diagnostics and prognostics is an emerg-ing technology in performing effective rotatingmachinery maintenance. In the last decade,progress has been made in machinery diagnostics,which includes sensing techniques, sensor develop-ment, and advanced signal processing techniques.There is a need to speed up the signal processingand analyses to provide real-time and on-line

capability. Wireless communication and remote

sensing techniques show some promise and shouldbe considered for implementation. Machineryprognostics is a very challenging task, and its

progress has not kept up with that of diagnostics.Accordingly, a better understanding of failuremechanisms, together with development ofphysics-based models, is needed to achieve theprognostics capability.Though the CBM approach is effective, the

cost of sensor arrays, related instrumentationand hardware, and the information system couldbe significant. To meet the objective of reducingoverall maintenance costs, innovative concepts andtechniques are required to develop, design andfabricate cost-effective sensors, signal processing

40 K.W. NG

algorithms, and information system software andrelated hardware. For example, virtual sensorconcept, and combined sensor and signal process-ing electronic chip could reduce cost and provideimproved system integration.

In addition to providing reliable information tomaintenance operators, the outputs of the CBMsystem have to be simple enough for the operatorsto understand and thus take timely correctiveactions. Accordingly, improvements are needed inman-machine interface and displa:y of outputs.

References

Cempel, C., 1991, Vibroacoustic Condition Monitoring, EllisHorwood.

Dimentberg, M.F., Frolov, K.V. and Menyailov, A.I., 1991,Vibroacoustical Diagnostics for Machines and Structures,Research Studies Press Ltd.

Lyons, R.H., 1987, Machinery Noise and Diagnostics,Butterworth-Heinemann.

Natke, H.G. and Cempel, C., 1996, Model-Aided Diagnosis ofMechanical Systems Fundamentals, Detection, Localization,and Assessment, Springer-Verlag.

Proceedings of the Society for Machinery Failure PreventionTechnology (MFPT, formerly MFPG), 1968-1997, VibrationInstitute.

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