Current status of machine prognostics in condition-based maintenance: a review

  • Ying Peng
  • Ming DongEmail author
  • Ming Jian Zuo


Condition-based maintenance (CBM) is a decision-making strategy based on real-time diagnosis of impending failures and prognosis of future equipment health. It is a proactive process that requires the development of a predictive model that can trigger the alarm for corresponding maintenance. Prognostic methodologies for CBM have only recently been introduced into the technical literature and become such a focus in the field of maintenance research and development. There are many research and development on a variety of technologies and algorithms that can be regarded as the steps toward prognostic maintenance. They are needed in order to support decision making and manage operational reliability. In this paper, recent literature that focuses on the machine prognostics has been reviewed. Generally, prognostic models can be classified into four categories: physical model, knowledge-based model, data-driven model, and combination model. Various techniques and algorithms have been developed depending on what models they usually adopt. Based on the review of some typical approaches and new introduced methods, advantages and disadvantages of these methodologies are discussed. From the literature review, some increasing trends appeared in the research field of machine prognostics are summarized. Furthermore, the future research directions have been explored.


Condition-based maintenance Prognostics 


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  1. 1.
    Holmberg K, Komonen K, Oedewald P, Peltonen M, Reiman T, Rouhiainen V, Tervo J, Heino P (2004) Safety and reliability technology review. Res Rep BTUO43-031209. VTT Industrial Systems, EspooGoogle Scholar
  2. 2.
    Tu PYL, Yam R, Tse P, Sun AOW (2001) An integrated maintenance management system for an advanced manufacturing company. Int J Adv Manuf Technol 17:692–703CrossRefGoogle Scholar
  3. 3.
    Gertsbakh IB (1977) Models of preventive maintenance. North-Holland, AmsterdamzbMATHGoogle Scholar
  4. 4.
    Tu Y (1995) Decision support system for equipment diagnosis and maintenance management: an artificial intelligent approach. Research Proposal, City University of Hong Kong, HKGoogle Scholar
  5. 5.
    Katipamula S, Brambley MR (2005) Methods for fault detection, diagnostics, and prognostics for building systems—a review, part I. HVAC&R Res 11(1):3–25Google Scholar
  6. 6.
    Jardine AKS, Daming L, Dragan B (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Sys Signal Process 20:1483–1510CrossRefGoogle Scholar
  7. 7.
    Schwabacher M, Goebel K (2005) A survey of artificial intelligence for prognostics. AAAI Fall Symposium—Tech Rep, pp 107–114Google Scholar
  8. 8.
    Zhang L, Li XS (2006) A review of fault prognostics in condition based maintenance. The Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-electronic Technology, and Artificial Intelligence, 13–15 Oct 2006, Beijing (China), vol 6357, art. no. 635752Google Scholar
  9. 9.
    Kothamasu R, Huang SH, VerDuin WH (2006) System health monitoring and prognostics—a review of current paradigms and practices. Int J Adv Manuf Technol 28:1012–1024CrossRefGoogle Scholar
  10. 10.
    Goh KM, Tjahj B, Baines T, Subramaniam S (2006) A review of research in manufacturing prognostics. 2006 IEEE Int Conf on Ind, 16–18 Aug. 2006, Singapore (Singapore), vol 16–18, pp 417–422Google Scholar
  11. 11.
    Brotherton T, Jahns J, Jacobs J, Wroblewski D (2000) Prognosis of faults in gas turbine engines. Proceedings of the IEEE Aerospace Conf, 18–25 Mar. 2000, Big Sky, MT (USA), vol 6, pp 163–171Google Scholar
  12. 12.
    Li Y, Billington S, Zhang C, Kurfess T, Danyluk S, Liang S (1999) Adaptive prognostics for rolling element bearing condition. Mech Sys Signal Process 13:103–113CrossRefGoogle Scholar
  13. 13.
    Engel SJ, Gilmartin BJ, Bongort K, Hess A (2000) Prognostics, the real issues involved with predicting life remaining. Proceedings of the IEEE Aerospace Conf, 18–25 Mar. 2000, Big Sky, MT (USA), vol 6, pp 457–469Google Scholar
  14. 14.
    Kacprzynski GJ, Gumina M, Roemer MJ, Caguiat DE (2001) A prognostic modeling approach for predicting recurring maintenance for shipboard propulsion systems. Proceedings of ASME Turbo Expo, 4–7 June 2001, New Orleans, LA (USA), P884310Google Scholar
  15. 15.
    Oppenheimer CH, Loparo KA (2002) Physically based diagnosis and prognosis of cracked rotor shafts. Proceedings of SPIE Compo and Sys Diagnostics, Prognostics, and Health Management II, vol 4733, pp 122–132Google Scholar
  16. 16.
    Luo J, Bixby A, Pattipati K, Liu Q, Kawamoto M, Chigusa S (2003) An interacting multiple model approach to model-based prognostics. Syst Man Cybern 1:189–194Google Scholar
  17. 17.
    Kacprzynski GJ, Sarlashkar A, Roemer MJ (2004) Predicting remaining life by fusing the physics of failure modeling with diagnostics. J Met 56:29–35Google Scholar
  18. 18.
    Byington CS, Watson M, Edwards D, Stoelting P (2004) A model-based approach to prognostics and health management for flight control actuators. Proceedings of the IEEE Aerospace Conf, 6–13 Mar 2004, Big Sky, MT (USA), vol 6, pp 3351–3362Google Scholar
  19. 19.
    Cempel C, Natke HG, Tabaszewski M (1997) A passive diagnostic experiment with ergodic properties. Mech Sys Signal Process 11:107–117CrossRefGoogle Scholar
  20. 20.
    Qiu J, Zhang C, Seth BB, Liang SY (2002) Damage mechanics approach for bearing lifetime prognostics. Mech Sys Signal Process 16:817–829CrossRefGoogle Scholar
  21. 21.
    Garga AK, McClintic KT, Campbell RL, Yang CC, Lebold MS, Hay TA, Byington CS (2001) Hybrid reasoning for prognostic learning in CBM systems. Proceedings of the IEEE Aerospace Conf, 10–17 Mar 2001, Big Sky, MT (USA), vol 6, pp 2957–2969Google Scholar
  22. 22.
    David JM, Krivine JP (1987) Three artificial intelligence issues in fault diagnosis: declarative programming, expert systems, and model-based reasoning. Proceedings of the Sec Euro Workshop on Fault Diagnostics, Reliability and Related Knowledge Based Approaches, vol 6–8, pp 190–196Google Scholar
  23. 23.
    Lembessis E, Antonopoulos G, King RE, Halatsis C, Torres J (1989) ‘CASSANDRA’: an on-line expert system for fault prognosis. Proceedings of the 5th CIM Euro Conf, 1989, pp 371–377Google Scholar
  24. 24.
    Butler KL (1996) An expert system based framework for an incipient failure detection and predictive maintenance system. Proceedings of the Int Conf on Intelligent Sys Applications to Power Sys, ISAP, 28 Jan–2 Feb 1996, Orlando, FL (USA), pp 321–326Google Scholar
  25. 25.
    Biagetti T, Sciubba E (2004) Automatic diagnostics and prognostics of energy conversion processes via knowledge based systems. Energy 29(12–15):2553–2572CrossRefGoogle Scholar
  26. 26.
    Choi SS, Kang KS, Kim HG, Chang SH (1995) Development of an on-line fuzzy expert system for integrated alarm processing in nuclear power plants. IEEE Trans Nucl Sci 42(4):1406–1418CrossRefGoogle Scholar
  27. 27.
    Frelicot C (1996) A fuzzy-based prognostic adaptive system. RAIRO-APII-JESA. J Eur Sys Autom 30(2–3):281–299Google Scholar
  28. 28.
    Luo J, Namburu M, Pattipati K, Liu Q, Kawamoto M, Chigusa S (2003) Model-based prognostic techniques [maintenance applications]. Proceedings of AUTOTESTCON, IEEE Sys Readiness Tech Conf, 22–25 Sep 2003, Anaheim, CA (USA), vol 22–25, pp 330–340Google Scholar
  29. 29.
    Ray A, Tangirala S (1996) Stochastic modeling of fatigue crack dynamics for on-line failure prognostics. IEEE Trans Control Sys Technol 4:443–451CrossRefGoogle Scholar
  30. 30.
    Wang W (2002) A model to predict the residual life of rolling element bearings given monitored condition information to date. IMA J Manag Math 13:3–16zbMATHCrossRefMathSciNetGoogle Scholar
  31. 31.
    Yan J, Koc M, Lee J (2004) A prognostic algorithm for machine performance assessment and its application. Prod Plan Control 15:796–801CrossRefGoogle Scholar
  32. 32.
    Banjevic D, Jardine AKS (2006) Calculation of reliability function and remaining useful life for a Markov failure time process. IMA J Manag Math 17(2):115–130zbMATHCrossRefMathSciNetGoogle Scholar
  33. 33.
    Goebel K, Saha B, Saxena A (2005) Prognostics in battery health management. IEEE Instrum Meas Mag 11(4):33–40CrossRefGoogle Scholar
  34. 34.
    Gebraeel NZ, Lawley MA, Li R, Ryan JK (2005) Residual-life distributions from component degradation signals: a Bayesian approach. IIE Trans 37:543–557CrossRefGoogle Scholar
  35. 35.
    Tsoukalas LH, Uhrig RE (1997) Fuzzy and neural approaches in engineering. Wiley, New YorkGoogle Scholar
  36. 36.
    Connor JT, Matinem RD, Atlas LE (1994) Recurrent neutral networks and robust time series prediction. IEEE Trans Neural Netw 5:240–254CrossRefGoogle Scholar
  37. 37.
    Zhang S, Ganesan R (1997) Multivariable trend analysis using neural networks for intelligent diagnostics of rotating machinery. Trans ASME J Eng Gas Turbine Power 119:378–384CrossRefGoogle Scholar
  38. 38.
    Yam RCM, Tse PW, Li L, Tu P (2001) Intelligent predictive decision support system for condition-based maintenance. Int J Adv Manuf Technol 17:383–391CrossRefGoogle Scholar
  39. 39.
    Byington CS, Watson M, Edwards D (2004) Data-driven neural network methodology to remaining life predictions for aircraft actuator components. Proceedings of the IEEE Aerospace Conf, 6–13 Mar 2004, Big Sky, MT (USA), vol 6, pp 3581–3589Google Scholar
  40. 40.
    Khawaja T, Vachtsevanos G, Wu B (2005) Reasoning about uncertainty in prognosis: a confidence prediction neural network approach. Annual Conf of the North American Fuzzy Inf Proc Society—NAFIPS, 26–28 Jun 2005, Detroit, MI (USA), vol 2005, pp 7–12Google Scholar
  41. 41.
    Yu G, Qiu H, Djurdjanovic D, Jay L (2006) Feature signature prediction of a boring process using neural network modeling with confidence bounds. Int J Adv Manuf Technol 30:614–621CrossRefGoogle Scholar
  42. 42.
    Wang P, Vachtsevanos G (2001) Fault prognosis using dynamic wavelet neural networks. Artif Intell Eng Des Anal Manuf (AIEDAM) 15(4):349–365zbMATHGoogle Scholar
  43. 43.
    Gebraeel NZ, Lawley MA (2008) A neural network degradation model for computing and updating residual life distributions. IEEE Trans Autom Sci Eng 5(1):387–401CrossRefGoogle Scholar
  44. 44.
    Parker BE, Nigro TM, Carley MP, Barron RL, Ward DG, Poor HV, Rock D (1993) Helicopter gearbox diagnostics and prognostics using vibration signature analysis. Proceedings of the SPIE—The Int Society for Optical Eng, 13 April 1993, Orlando, FL (USA), vol 1965, pp 531–542Google Scholar
  45. 45.
    Kohonen T (1995) Self-organizing maps. Springer, BerlinGoogle Scholar
  46. 46.
    Jamsa-Jounela SL, Vermasvuori M, Enden P, Haavisto S (2003) A process monitoring system based on the Kohonen self-organizing maps. Control Eng Pract 11:83–92CrossRefGoogle Scholar
  47. 47.
    Huang RQ, Xi LF (2007) Residual life predictions for ball bearing based on neural networks. Chinese Journal of Mechanical Engineering 43(10):137–143CrossRefGoogle Scholar
  48. 48.
    Liu Y, Li SQ (2007) Decision support for maintenance management using Bayesian networks. Int Conf on Wireless Communications, Networking and Mobile Computing, art. no. 4341175, 5708–5711Google Scholar
  49. 49.
    Sheppard JW, Kaufman MA (2005) Bayesian diagnosis and prognosis using instrument uncertainty. Proceedings of AUTOTESTCON, 26–29 Sep 2005, Orlando, FL (USA), vol 2005, pp 417–423Google Scholar
  50. 50.
    Przytula KW, Choi A (2007) Reasoning framework for diagnosis and prognosis. Proceedings of 2007 IEEE Aerospace Conf, 3–10 Mar 2007, Big Sky, MT (USA), art. no. 4161649Google Scholar
  51. 51.
    Dong M, Yang ZB (2008) Dynamic Bayesian network based prognosis in machining processes. J Shanghai Jiaotong Univ (Sci) 13(3):318–322CrossRefGoogle Scholar
  52. 52.
    Bunks C, McCarthy D, Al-Ani T (2000) Condition-based maintenance of machines using hidden Markov models. Mech Sys Signal Process 14(4):597–612CrossRefGoogle Scholar
  53. 53.
    Zhang XD, Xu R, Chiman K, Liang SY, Xie QL, Haynes L (2005) An integrated approach to bearing fault diagnostics and prognostics. Proceedings of the 2005 American Control Conf, 8–10 Jun 2005, Portland, OR (USA), vol 4, pp 2750–2755Google Scholar
  54. 54.
    Baruah P, Chinnam RB (2003) HMMs for diagnostics and prognostics in machining processes. Int J Prod Res 43(6):1275–1293CrossRefGoogle Scholar
  55. 55.
    Camci F (2005) Process monitoring, diagnostics and prognostics using support vector machines and hidden Markov models. Graduate School of Wanye State University, DetroitGoogle Scholar
  56. 56.
    Gu HY, Tseng CY, Lee LS (1991) Isolated-utterance speech recognition using hidden Markov models with bounded state duration. IEEE Trans Signal Process 39(8):1743–1751CrossRefGoogle Scholar
  57. 57.
    Falaschi A (1992) Continuously variable transition probability HMM for speech recognition. In: Laface P, De Mori R (eds) Speech recognition and understanding. Springer, Berlin, pp 125–130Google Scholar
  58. 58.
    Dong M, He D, Banerjee P, Keller J (2006) Equipment health diagnosis and prognosis using hidden semi-Markov models. Int J Adv Manuf Technol 30:738–749CrossRefGoogle Scholar
  59. 59.
    Dong M, He D (2007) Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis. Eur J Oper Res 178(3):858–878zbMATHCrossRefGoogle Scholar
  60. 60.
    Dong M, He D (2007) A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology. Mech Sys Signal Process 21(5):2248–2266CrossRefGoogle Scholar
  61. 61.
    Kumar D, Klefsjo B (1994) Proportional hazards model: a review. Reliab Eng Sys Saf 44:177–188CrossRefGoogle Scholar
  62. 62.
    Bebbington M, Lai CD, Zitikis R (2008) Reduction in mean residual life in the presence of a constant competing risk. Appl Stoch Models Bus Ind 24(1):51–63zbMATHCrossRefMathSciNetGoogle Scholar
  63. 63.
    Victor L, Riquelme M, Balakrishnan N, Sanhueza A (2008) Lifetime analysis based on the generalized Birnbaum–Saunders distribution. Comput Stat Data Anal 52:2079–2097zbMATHCrossRefGoogle Scholar
  64. 64.
    Cox DR (1972) Regression models and life-tables. J R Stat Soc 134:187–220Google Scholar
  65. 65.
    Lloyd GM, Hasselman T, Paez T (2005) A proportional hazards neural network for performing reliability estimates and risk prognostics for mobile systems subject to stochastic covariates. Eng/Tech Management, Safety Eng and Risk Analysis, Tech and Soc, Eng Bus Management, Health and Safety 2005, pp 97–106Google Scholar
  66. 66.
    Liao H, Qiu H, Lee J, Lin D, Banjevic D, Jardine A (2005) A predictive tool for remaining useful life estimation of rotating machinery components. Proceedings of the ASME Int Design Eng Tech Conf and Computers and Inf in Eng Conf-DETC, 24–28 Sep 2005, Long Beach, CA (USA), vol 1A, pp 509–515Google Scholar
  67. 67.
    Li ZG, Zhou S, Choubey S, Sievenpiper C (2007) Failure event prediction using the Cox proportional hazard model driven by frequent failure signatures. IIE Trans 39:303–315CrossRefGoogle Scholar
  68. 68.
    Deng JL (1989) Introduction to grey system theory. J Grey Syst 1(1):1–24zbMATHGoogle Scholar
  69. 69.
    Huang YP, Huang CC, Hung CH (1994) Determination of the preferred fuzzy variables and applications to the prediction control by the grey modeling. The Sec National Conf on Fuzzy Theory and Application, Taipei (Taiwan), pp 406–409Google Scholar
  70. 70.
    Ku LL, Huang TC (2006) Sequential monitoring of manufacturing processes: an application of grey forecasting models. Int J Adv Manuf Technol 27(5–6):543–546CrossRefGoogle Scholar
  71. 71.
    Gu J, Bilal M, Pecht M (2008) Grey prediction method used in failure prognostics for electronics.
  72. 72.
    He QH, He XY, Zhu JX (2008) Fault detection of excavator’s hydraulic system based on dynamic principal component analysis. J Cent South Univ Technol 15:700–705CrossRefGoogle Scholar
  73. 73.
    Lee JM, Yoo CK, Choi SW, Vanrolleghem PA (2004) Nonlinear process monitoring using kernel principal component analysis. Chem Eng Sci 59:223–234CrossRefGoogle Scholar
  74. 74.
    Nomikos P, MacGregor JF (2004) Monitoring batch processes using multi-way principal component analysis. AIChE J 40:1361–1375CrossRefGoogle Scholar
  75. 75.
    Kwan C, Zhang X, Xu R, Haynes L (2003) A novel approach to fault diagnostics and prognostics. Proceedings of the 2003 IEEE Int Conf on Robotics and Automation, 14–19 Sep 2003, Taipei (Taiwan), vol 1, pp 604–609Google Scholar
  76. 76.
    Lee JM, Yoo CK, Lee IB (2003) On-line batch process monitoring using a consecutively updated multiway principal component analysis model. Comput Chem Eng 27:1903–1912CrossRefGoogle Scholar
  77. 77.
    Wang WQ, Golnaraghi MF, Ismail F (2004) Prognosis of machine health condition using neuro-fuzzy systems. Mech Sys Signal Process 18:813–831CrossRefGoogle Scholar
  78. 78.
    Chinnam RB, Baruah P (2004) A neuro-fuzzy approach for estimating mean residual life in condition-based maintenance systems. Int J Mater Prod Technol 20:166–179CrossRefGoogle Scholar
  79. 79.
    Satish B, Sarma NDR (2005) A fuzzy BP approach for diagnosis and prognosis of bearing faults in induction motors. IEEE Power Eng Soc General Meeting, 12–16 Jun 2005, San Francisco, CA (USA), vol 3, pp 2291–2294Google Scholar
  80. 80.
    Xue GX, Xiao LC, Bie MH, Lu SW (2005) Fault prediction of boilers with fuzzy mathematics and RBF neural network. Proceedings of Int Conf on Communications, Circuits and Sys, 27–30 May 2005, Hong Kong (China), vol 2, pp 1012–1016Google Scholar
  81. 81.
    Kothamasu R, Huang SH (2007) Adaptive Mamdani fuzzy model for condition-based maintenance. Fuzzy Sets Syst 158:2715–2733CrossRefMathSciNetGoogle Scholar
  82. 82.
    Chen MY (1990) Uncertainty analysis and grey modeling. Proceedings of the 1st International Symposium on Uncertainty Modeling Analysis, pp 469–473Google Scholar
  83. 83.
    Dong YL, Gu YJ, Yang K, Zhang WK (2004) A combining condition prediction model and its application in power plant. Proceedings of the Int Conf on Machine Learning and Cybernetics, 26–29 Aug 2004, Shanghai (China), vol 6, pp 3474–3478Google Scholar
  84. 84.
    Shetty P, Mylaraswamy D, Ekambaram T (2008) A hybrid prognostic model formulation and health estimation of auxiliary power units. J Eng Gas Turbine Power 130(2):021601CrossRefGoogle Scholar
  85. 85.
    Mohanty S, Chattopadhyay A, Peralta P, Das S, Willhauck C (2008) Fatigue life prediction using multivariate Gaussian process. Collection of Tech Papers AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conf, 7–10 Apr 2008, Schaumburg, IL (USA), art. no. 2008-1837Google Scholar
  86. 86.
    Shao Q, Shao C, Feng CJ (2008) Identification of non-stationary time series based on SVM-HMM method. Proceedings of 2008 IEEE Int Conf on Service Operations and Logistics, and Informatics, 12–15 Oct 2008, Beijing, China, vol 1, pp 293–298Google Scholar
  87. 87.
    Elwany AH, Gebraeel NZ (2008) Sensor-driven prognostic models for equipment replacement and spare parts inventory. IIE Trans 40(7):629–639CrossRefGoogle Scholar

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© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.Antai College of Economics & ManagementShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  2. 2.Department of Mechanical EngineeringUniversity of AlbertaEdmontonCanada

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