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System Health Monitoring and Prognostics – A Review of Current Paradigms and Practices

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Abstract

The oldest and most common maintenance and repair strategy is “fix it when it breaks”. The appeal of this approach is that no analysis or planning is required. The problems with this approach include the occurrence of unscheduled downtime at times that may be inconvenient, perhaps preventing accomplishment of committed production schedules. Unscheduled downtime has more serious consequences in applications such as aircraft engines. These problems provide motivation to perform maintenance and repair before the problem arises. The simplest approach is to perform maintenance and repair at pre-established intervals, defined in terms of elapsed or operating hours. This strategy can provide relatively high equipment reliability, but it tends to do so at excessive cost (higher scheduled downtimes). A further problem with time-based approaches is that failures are assumed to occur at specific intervals.

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References

  • Abdulnour G, Dudek R A, Smith M L (1995) Effect of Maintenance Policies on the Just-in-Time production System. Int J of Prod Res, 33: 565–583.

    Article  MATH  Google Scholar 

  • Albino V, Carella G, Okogbaa OG (1992) Maintenance policies in Just-In-Time Manufacturing Lines. Int J of Prod Res, 30: 369–382.

    Article  MATH  Google Scholar 

  • Al-Hassan,K, Swailes DC, Chan JFL, Metcalfe AV (2000) Markov models for promoting total productive maintenance. Proc Ind Sta In Action, 1: 1–12.

    Google Scholar 

  • Al-Hassan K, Swailes DC, Chan JFL, Metcalfe AV (2002) Supporting maintenance strategies using markov models. IMA J of Manag Math 13: 17–27.

    Article  MATH  MathSciNet  Google Scholar 

  • Almutawa S, Moon YB (1999) The Development of a connectionist expert system for compensation of color deviation in offset lithographic printing. Artif Intell in Eng 13: 427–434.

    Article  Google Scholar 

  • Alonso GC, Acosta G, Mira J, De Prada C (1998) Knowledge-based process control supervision and diagnosis: the AEROLID approach. Expert Syst with Appl 14: 371–383.

    Article  Google Scholar 

  • Alonso GC, Pulido JB, Acosta LG, Llamas BC (2001) On-line industrial supervision and diagnosis, knowledge level description and experimental results. Expert Syst with Appl 20: 117–132.

    Article  Google Scholar 

  • Arroyo-Figueroa G, Alvarez Y, Sucar E (2000) SEDRET – an intelligent system for the diagnosis and prediction of events in power plants. Expert Syst with Appl 18: 75–86.

    Article  Google Scholar 

  • Balle P, Fuessel D (2000) Closed –loop fault diagnosis-based on a nonlinear process model and automatic fuzzy rule generator. Eng Appl of Artif Intell 13: 695–704.

    Article  Google Scholar 

  • Basseville M (1988) Detecting changes in signals an systems – A Survey. Int Fed of Autom Control, 24(3): 309–326.

    MATH  MathSciNet  Google Scholar 

  • Beard RV (1971) Failure accommodation in linear systems through self-reorganization. PhD Thesis, MIT, USA.

    Google Scholar 

  • Ben-Daya M, Duffua, SO (1995) Maintenance and quality: the missing link. J of Qual in Maint Eng 1(1): 20–26.

    Article  Google Scholar 

  • Ben-Daya M, Rahim MA (2000) Effect of maintenance on the economic design of x-chart. The Eur J of Oper Res 120: 131–143.

    Article  MATH  Google Scholar 

  • Ben-Daya M, Duffua SO, Raouf A (2000) Overview of Maintenance Modeling Areas. Maintenance, Modeling And Optimization, Kluwer Academic Publishers, Massachusetts, pp 3–35.

    Google Scholar 

  • Berec L (1998) A multi-model method for failure diagnosis and detection: Bayesian solution, An introductory treatise. Int J Adapt Control Signal Process, 12: 81–92.

    Article  MATH  MathSciNet  Google Scholar 

  • Blanchard B, Verma D, Peterson EL (1995) Maintanability: A key to effective serviceability and Maintenance Management. John Wiley & Sons, New York, NY.

    Google Scholar 

  • Bunks C, McCarthy D, Al-Ani T (2000) Condition-based Maintenance of machines using hidden markov models. Mechanical Systems and Signal Processing, 14(4): 597–612.

    Article  Google Scholar 

  • Chen J, Patton RJ (1999) Robust Model-based Fault Diagnosis for Dynamic Systems. Kluwer Academic, New York.

    MATH  Google Scholar 

  • Chen J, Patton RJ (2000) Standard H filter formulation of robust fault detection. 4th IFAC symposium on Fault Detection, Supervision and Safety for Technical Processes, 1: 256–261.

    Google Scholar 

  • Chen J, Patton RJ (2001) Fault-tolerant control systems design using the linear matrix inequality method. European Control Conference, ECC’01:1993–1998.

    Google Scholar 

  • Chen S, Billin AS, Cowan CFN, Grant P (1990) Practical identification of NARMAX model using radial basis functions. Int J Control 52: 1327–1350.

    Article  MATH  Google Scholar 

  • Chen J, Patton RJ, Liu GP (1996a) Optimal residual design for fault-diagnosis using multiobjective optimization and genetic algorithms. Int J Syst Sci, 27(6): 567–576.

    Article  MATH  Google Scholar 

  • Chen J, Patton RJ, Zhang HY (1996b) Design of unknown input observer and robust fault detection filters. Int J Control, 63(1): 85–105.

    Article  MATH  MathSciNet  Google Scholar 

  • Chow EY, Wilsky AS (1984) Analytical redundancy and the design of robust failure detection sytems. IEEE Trans on Autom control 29(7): 603–614.

    Article  MATH  Google Scholar 

  • Deckert JC, Desau MN, Deyst JJ, Wilsky AS (1977) DFBW sensor failure identification using analytic redundancy. IEEE Trans Autom Control 22: 795–809.

    Article  Google Scholar 

  • Ding X, Frank PM (1990) Fault detection via factorization approach. Syst Control Lett 14(5): 431–436.

    Article  MATH  MathSciNet  Google Scholar 

  • Ding X, Frank PM (1991) Frequency domain approach and threshold selector for robust model-based fault detection and isolation. In Preprint of IFAC/IMACS symposium SAFEPROCESS’91, 1: 307–312.

    Google Scholar 

  • Duan GR, Patton RJ (2001) Robust fault detection using Luenberger-type unknown input observers: a parametric approach. Int J Syst Sci 32(4): 533–540.

    Article  MATH  MathSciNet  Google Scholar 

  • Eisenmann, R Sr, Eisenmann, R Jr (1997) Applied condition monitoring. mach malfunct diagn and correct 13: 703–741.

    Google Scholar 

  • Enbo F, Haibin Y, Ming R (1998) Fuzzy expert system for real-time process condition monitoring and incident prevention. Expert Syst with Appl 15: 383–390.

    Article  Google Scholar 

  • Eshleman RL (1999) Basic Machinery Vibrations. VIPress Inc, Clarendon Hills, IL.

    Google Scholar 

  • Feigl P, Zelen M (1965) Estimation of exponential survival probabilities with concomitant information. Biom 21: 826–838.

    Google Scholar 

  • Geng Z, Qu L (1994) Vibrational diagnosis of machine parts using the wavelet packet technique. Insight 36: 11–15.

    Google Scholar 

  • Gideon C (1998) Neural networks implementations to control real-time manufacturing systems. Comput Integr Manuf Syst 11: 243–251.

    Article  Google Scholar 

  • Gopalan, MN, Kumar D (1995) Analysis of cold-standby systems cutting and clustering the state space. Int J of Qual Reliab and Saf Eng 2(3): 327–340.

    Article  Google Scholar 

  • Gorzalczany BM (1999) On some idea of a neuro-fuzzy controller. Inf Sci 120: 69–87.

    Article  Google Scholar 

  • Hans L (1999) Management of industrial maintenance – economic evaluation of maintenance policies. Int J of Oper & Prod Manag 19(7): 716–737.

    Article  Google Scholar 

  • Hickman et al. (1989) Analysis for knowledge-based systems: A practical guide to the KADS methodology. Prentice Hall, Chinchester, UK.

    MATH  Google Scholar 

  • Husband TM (1978) Maintenance Management and Terotechnology. Gower Publishing, Aldershot, UK.

    Google Scholar 

  • Hussain MA (1999) Review of the applications of neural networks in chemical process control-simulation and online implementation. Artif Intell in Eng 13: 55–68.

    Article  MATH  Google Scholar 

  • Isermann R (1993) Fault diagnosis via parameter estimation and knowledge processing. Autom 29(4): 815–835.

    Article  MATH  MathSciNet  Google Scholar 

  • Jardine AKS (1987) Maintenance, Replacement and Reliability, Pitman, Boston, MA.

    Google Scholar 

  • Jones RB (1995) Risk-based Management: a reliability centered approach. Gulf Publishers, Houston.

    Google Scholar 

  • Kazamian BH (2001). Study of Learning Fuzzy Controllers. Expert Syst, 18(4): 186–193.

    Article  Google Scholar 

  • Kerestecioglu F (1993) Change Detection and Input design in dynamical systems. Research Studies Press, Taunton, UK.

    Google Scholar 

  • Kerestecioglu F, Zarrop MB (1994) Input Design for abrupt changes in dynamical systems. Int J of Control 59(4): 1063–1084.

    Article  MATH  MathSciNet  Google Scholar 

  • Kinclaid LR (1987) Evolution of condition monitoring and the management of maintenance. Proceedings of International Conference on Condition Monitoring, 1: 13–21.

    Google Scholar 

  • Kitamura M (1980) Detection of sensor failures in nuclear plant using analytic redundancy. Trans Am Nucl Soc 34: 581–583.

    MathSciNet  Google Scholar 

  • Knezevic J (1987) Condition parameter-based approach to calculation of reliability characteristics. Reliab Eng 19 (1): 29–39.

    Article  Google Scholar 

  • Kobbacy KAH, Fawzy BB, Percy DF (1997) A full history of proportional hazards model for preventive maintenance scheduling. Qual and Reliab Eng Int 13: 187–198.

    Article  Google Scholar 

  • Kumar U, Granholm S (1990) Reliability centered maintenance – a tool for higher profitability. Maint 5(3): 23–26.

    Google Scholar 

  • Lau HCW, Wong TT, Ning A (2001) Incorporating machine intelligence in a parameterbased control system: a neural-fuzzy approach. Artif Intell in Eng 15(3): 253–264.

    Article  Google Scholar 

  • Lebold M, Reichard Karl, Boylan D (2003) Using DCOM in an Open System Architecture Framework for Machinery monitoring and Diagnostics. IEEE Aerospace Conference, 3: 1227–1235.

    Google Scholar 

  • Leger R P, Garland WJ, Poehlman WFS (1998) Fault detection and diagnosis using statistical control charts and artificial neural networks. Artif Intell in Eng 12: 35–47.

    Article  Google Scholar 

  • Leung D, Ramanougli J (2000) Dynamic probabilistic model-based expert system for fault diagnosis. Computers and Chem Eng 24: 2473–2492.

    Article  Google Scholar 

  • Lin J, Qu L (2000) Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis. J of Sound and Vib 234: 135–148.

    Article  Google Scholar 

  • Logan D, Joseph M (1994) Using the correlation dimension for the vibration fault diagnosis of rolling element bearings. Mech Syst and Signal Process 10(3): 241–250.

    Article  Google Scholar 

  • Makis V (1998) Optimal lot sizing and inspection policy for an EMQ model with imperfect inspections. Nav Res Logist 45: 165–186.

    Article  MATH  MathSciNet  Google Scholar 

  • Malik MAK (1979) Reliable preventive maintenance scheduling. AIIE Trans 11:221–228.

    Google Scholar 

  • McFadden PD (1986) Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration. J of Vib Acoust Stress and Reliab in Des 108: 165–170.

    Google Scholar 

  • McFadden PD (1987) Examination of a technique for early detection of failure in gears by signal processing of the time domain average of the meshing vibration. Mech Syst and Signal Process 1: 173–183.

    Article  Google Scholar 

  • McFadden PD (1994) Application of the wavelet transform to early detection of gear failure by vibration analysis. Proceedings of International conference of condition monitoring,1, 172–183.

    Google Scholar 

  • McFadden PD, Smith JD (1985) A signal processing technique for detecting local defects in a gear from signal average of vibration. Proceedings of Institute of Mechanical Engineers, 199(c4): 287–292.

    Google Scholar 

  • McFadden PD, Wang WJ (1993) Early detection of gear failure by vibration analysis-I, calculation of the time-frequency distribution. Mech Syst and Signal Process 7: 193–203.

    Article  Google Scholar 

  • McFadden PD, Wang WJ (1996) Application of wavelets to gearbox vibration signals for fault detection. J of Sound and Vib 7: 193–203.

    Google Scholar 

  • Mitchell J, Bond T, Bever K, Manning N (1998) MIMOSA Four Years Later. Sound and Vib 12–21.

    Google Scholar 

  • Mobley RK (1990) An Introduction to Preventive Maintenance. Plant Engineering Series, Van Nostrand Reinhold, New York.

    Google Scholar 

  • Monderres M (1993) What every engineer should know about reliability and risk analysis, M.Dekker, New York.

    Google Scholar 

  • Moss MA (1985) Designing for minimal maintenance expense. Marcel Drekker Inc, New York.

    Google Scholar 

  • Moubray J (1997) Reliability Centered Maintenance. Industrial Press, New York.

    Google Scholar 

  • Nakajima S (1988) Total Productive Maintenance. Productivity Press, Cambridge, Massachusetts.

    Google Scholar 

  • Nikoukhah R (1998) Guaranteed active failure detection and isolation for linear dynamical systems. Autom 34(11): 348–1358

    Google Scholar 

  • Nikoukhah R, Campbell SL, Delebecque F (2000) Detection signal design for failure design: A robust approach. Int J of Adapt Control and Signal Process 14: 701–724.

    Article  MATH  Google Scholar 

  • Nooteboom P, Leemeijer GB (1993) Focusing based on the structure of a model in modelbased diagnosis. Intl J Man-Machine Studies 38: 455–474.

    Article  Google Scholar 

  • Ollila A, Malmipuro M (1999) Maintenance has a role in quality. The TQM Magazine, 11(1): 17–21.

    Article  Google Scholar 

  • Patton RJ (1994) Robust model-based fault diagnosis: the state-of-the-art. Proceedings of IFAC Symposium on Fault Detection, supervision and Safety for Processes (SAFEPROCESS),1,1:24.

    Google Scholar 

  • Patton RJ, Chen J (1994) A review of parity space approaches to fault diagnosis for aerospace systems. AIAA J of Guid Control & Dyn 17(2): 278–285.

    Article  MATH  Google Scholar 

  • Patton RJ, Chen J (1997) Observer-based fault detection and isolation: Robustness and applications. Control Eng Pract 5(5): 671–682.

    Article  Google Scholar 

  • Paya B, Esat I, Badi MNM (1995) Neural network-based fault detection using different signal processing techniques as pre-processor. JN Am Soc of Mech Eng PD Publ 70: 97–101.

    Google Scholar 

  • Paya BA, Esat II, Badi MNM (1997) Artificial Neural network-based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor. Mech Syst and Signal Process 11(5): 751–765.

    Article  Google Scholar 

  • Pena E, Hollander M, (1995) Dynamic reliability models with conditional proportional hazards. Lifetime Data Anal 1: 377–401.

    Article  MATH  MathSciNet  Google Scholar 

  • Rao BKN (1996) The need for condition monitoring and maintenance management in industries. Handbook of condition monitoring, Elsevier Science, Amsterdam, pp 1–36.

    Google Scholar 

  • Rao SS (1992) Reliability-based Design. McGraw Hill, New York.

    Google Scholar 

  • Ray LR, Townsend JR, Ramasubramanian A (2001) Optimal filtering and bayesian detection for friction-based diagnostics in machines. ISA Trans 40: 207–221.

    Article  Google Scholar 

  • Reiche H (1994) Maintenance minimization for competitive advantage. Gordon and Breach Science Publishers, Amsterdam, pp 49–54.

    Google Scholar 

  • Reiter R (1987) A theory of diagnosis from first principles. Artificial Intelligence, 32: 57–96.

    Article  MATH  MathSciNet  Google Scholar 

  • Sandtorv H (1991) RCM – Closing the loop between design, reliability and operational reliability. Maint 6(1): 13–21.

    Google Scholar 

  • Saranga H, Knezevic J (2000a) Reliability analysis using multiple relevant condition parameters. J of Qual in Maint Eng 6(3): 165–176.

    Article  Google Scholar 

  • Saranga H, Knezevic J (2000b) Reliability prediction for condition-based maintained systems. Reliab Eng Syst Saf 71, 219–224.

    Article  Google Scholar 

  • Sheu C, Krajewski LJ (1994) A decision model for corrective maintenance management. Int J of Prod Res 32 (6): 1365–1382.

    Article  MATH  Google Scholar 

  • Shiroshi J, Li Y, Liang S, Kurfess T, Danyluk S (1997) Bearing Condition Diagnostics via vibration and acoustic emission measurements. Mech Syst and Signal Process 11(5): 693–705.

    Article  Google Scholar 

  • Simani S, Fantuzii C, Patoon R (2003) Model-based Fault Diagnosis in Dynamic System Using Identification Techniques. Springer-Verlag, London, UK.

    Google Scholar 

  • Slany W, Vascak J (1996) A consistency checker for a fuzzy diagnosis system applied to warm rolling-mills in steelmaking plants. Proceedings of the 5th International Conference on Fuzzy Systems, 1: 206–212.

    Google Scholar 

  • Stamatis DH (1995) Failure Mode and Effect Analysis: FMEA from Theory to Execution. ASQC Quality Press, Milwaukee, Wisconsin.

    Google Scholar 

  • Staszewski WJ (1998) Wavelet-based Compression and feature selection for vibration analysis. J of Sound and Vib 211(5): 735–760.

    Article  Google Scholar 

  • Staszewski WJ, Tomlinson GR (1994) Application of wavelet transform to fault detection in a spur gear. Mech Syst and signal Process 8: 319–356.

    Article  Google Scholar 

  • Staszewski WJ, Worden K, Tomlinson GR (1997) Time-Frequency analysis in gearbox fault detection using the wigner-ville distribution and pattern recognition. Mech Syst and Signal Process 11(5): 673–692.

    Article  Google Scholar 

  • Struss P (1987) Multiple representation of structure and function, Expert Systems in Computer Aided Design. Elsevier Science Publishers, Amsterdam.

    Google Scholar 

  • Struss P (1988) A framework for model-based diagnosis. Siemens, AG, Technical report, INF ARM-10–88.

    Google Scholar 

  • Struss P (1989) Diagnosis as a process. In Hamscher et al. (eds). Readings in model-based diagnosis, Morgan Kauffman, San Mateo, pp 408–418.

    Google Scholar 

  • Struss P, Dressler O (1989) Physical negation: introducing fault models into the General Diagnostic Engine. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, 1318–1323.

    Google Scholar 

  • Sun Y (1994) Simulation for maintenance of an FMS: an Integrated system of maintenance and decision making. IJAMT 9:35–39.

    Google Scholar 

  • Tapiero, CS (1986) Continuous quality production and machine maintenance. Nav Res Logist Q 33: 489–499.

    Article  MATH  MathSciNet  Google Scholar 

  • Uosaki K, Tanaka I, Sugiyama H (1984) Optimal Input design for autoregressive model discrimination with constrained output. IEEE Trans on Automc control AC-29(4): 348–350.

    Article  Google Scholar 

  • Villanueva, H, Lamba H (1997) Operator Guidance System for industrial plant supervision. Expert Syst with Appl 12 (4): 441–454.

    Article  Google Scholar 

  • Wang CH, Sheung SH (2003) Determining the optimal production-inspection intervals with inspection errors: using a Markov chain. Comput & Oper Res 30 (2003): 1-17.

    Article  MathSciNet  Google Scholar 

  • Wang WJ (2001) Wavelets for detecting mechanical faults with high sensitivity. Mech Syst and Signal Process 5(14): 685–696.

    Article  Google Scholar 

  • Williams JH, Davies A, Drake PR (1994) Condition-based maintenance and machine diagnostics. Chapman & Hall, London, pp 1–18.

    Google Scholar 

  • Wilsky AS (1976) A survey of design methods for failure detection in dynamic systems. Autom 12(6): 601–611.

    Article  Google Scholar 

  • Wireman T (1998) Developing performance indicators for managing maintenance. Industrial Press, New York.

    Google Scholar 

  • Won JK, Modarres M (1998) Improved Bayesian method for diagnosing equipment partial failures in process plants. Comput Chem Eng 22(10): 1483–1502.

    Article  Google Scholar 

  • Wu X, Chen J, Wang W, Zhou Y (2001) Multi-Index Fusion-based Fault diagnosis theories and methods. Mech Syst and Signal Process 15(5): 995–1006.

    Article  Google Scholar 

  • Yam RCM, Tse PW, Li L, Tu P (2001) Intelligent predictive decision support system for condition-based maintenance. Int J of Adv Manuf Technol 17: 383–391.

    Article  Google Scholar 

  • Ying H (1998) The takagi-sugeno fuzzy controllers using the simplified linear control rules are nonlinear variable gain controllers. Autom, 34(2): 157–167.

    Article  MATH  Google Scholar 

  • Zhang H, Zhao G (1999) CMEOC – an expert system in the coal mining industry. Expert Syst with Appl 16 (1): 73–77.

    Article  MATH  Google Scholar 

  • Zhang XJ (1989) Auxiliary signal design in fault detection and diagnosis. Springer-Verlag, Hiedelberg, 1989.

    Book  MATH  Google Scholar 

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Authors and Affiliations

  1. University of Cincinnati, Cincinnati, OH, USA

    Ranganath Kothamasu & Samuel H. Huang

  2. Vertech LLC, Chagrin Falls, OH, USA

    William H. VerDuin

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  1. Ranganath Kothamasu
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  2. Samuel H. Huang
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Editors and Affiliations

  1. Department of Systems Engineering, King Fahd University of Petroleum & Minerals (KFUPM), 31261, Dhahran, Saudi Arabia

    Mohamed Ben-Daya  & Salih O. Duffuaa  & 

  2. University of Management and Technology, C-2 Johar Town, 54600, Lahore, Pakistan

    Abdul Raouf

  3. Mirce Akademy, Longbrook Street, EX4 6AP, Exeter, UK

    Jezdimir Knezevic

  4. Faculté de Sciences et de Génie, Département de Génie Mécanique, Université Laval, 1314E Pavillon Adrien-Pouliot, G1K 7P4, Sainte-Foy, QC, Canada

    Daoud Ait-Kadi

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Kothamasu, R., Huang, S., VerDuin, W. (2009). System Health Monitoring and Prognostics – A Review of Current Paradigms and Practices. In: Ben-Daya, M., Duffuaa, S., Raouf, A., Knezevic, J., Ait-Kadi, D. (eds) Handbook of Maintenance Management and Engineering. Springer, London. https://doi.org/10.1007/978-1-84882-472-0_14

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