Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 793161, 17 pages
http://dx.doi.org/10.1155/2015/793161
Review Article

Prognostics and Health Management: A Review on Data Driven Approaches

1Department of System Engineering and Engineering Management, City University of Hong Kong, Hong Kong
2Department of Industrial & Systems Engineering, National University of Singapore, Singapore 117576
3Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
4Faculty of Business and Law, Manchester Metropolitan University, Manchester M15 6BH, UK

Received 1 July 2014; Revised 25 October 2014; Accepted 31 October 2014

Academic Editor: Shaomin Wu

Copyright © 2015 Kwok L. Tsui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. P. MacDuffie and T. Fujimoto, “Why dinosaurs will keep ruling the auto industry,” Harvard Business Review, vol. 88, no. 6, pp. 23–25, 2010. View at Google Scholar · View at Scopus
  2. L. Layton, M. Glod, and L. H. Sun, Probe Finds Metro Control “Anomalies”, The Washingthon Post, Washington, DC, USA, 2009.
  3. J. Lyons, “Brazil blackout sparks infrastructure concerns,” The Washington Post, 2009.
  4. J. Finch, “Toyota sudden acceleration: a case study of the national highway traffic safety administration-recalls for change,” Loyola Consumer Law Review, vol. 22, p. 472, 2009. View at Google Scholar
  5. J. B. Bowles, “A survey of reliability-prediction procedures for microelectronic devices,” IEEE Transactions on Reliability, vol. 41, no. 1, pp. 2–12, 1992. View at Publisher · View at Google Scholar · View at Scopus
  6. K.-L. Tsui, W. Chiu, P. Gierlich, D. Goldsman, X. Liu, and T. Maschek, “A review of healthcare, public health, and syndromic surveillance,” Quality Engineering, vol. 20, no. 4, pp. 435–450, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Pecht, Prognostics and Health Management of Electronics, John Wiley & Sons, Hoboken, NJ, USA, 2008.
  8. F. Di Maio, J. Hu, P. Tse, M. Pecht, K. Tsui, and E. Zio, “Ensemble-approaches for clustering health status of oil sand pumps,” Expert Systems with Applications, vol. 39, no. 5, pp. 4847–4859, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. Bellcore, “Reliability prediction procedure for electronic equipment,” Tech. Rep. TR-NWT-000332, 83-86, 1990. View at Google Scholar
  10. M. Pecht and V. Ramappan, “Are components still the major problem. A review of electronic system and device field failure returns,” IEEE Transactions on Components, Hybrids, and Manufacturing Technology, vol. 15, no. 6, pp. 1160–1164, 1992. View at Publisher · View at Google Scholar · View at Scopus
  11. D. A. Thomas, K. Avers, and M. Pecht, “The “trouble not identified” phenomenon in automotive electronics,” Microelectronics Reliability, vol. 42, no. 4-5, pp. 641–651, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. H. Qi, S. Ganesan, and M. Pecht, “No-fault-found and intermittent failures in electronic products,” Microelectronics Reliability, vol. 48, no. 5, pp. 663–674, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. R. Williams, J. Banner, I. Knowles, M. Dube, M. Natishan, and M. Pecht, “An investigation of 'cannot duplicate' failures,” Quality and Reliability Engineering International, vol. 14, no. 5, pp. 331–337, 1998. View at Publisher · View at Google Scholar · View at Scopus
  14. K. Anderson, “Intermittent fault detection & isolation system,” in Proceedings of the Maintenance Symposium and Exhibition, Department of Defense, November 2012.
  15. A. G. Keane, Toyota Finds Most Sudden Acceleration Crashes Are Driver Error, Bloomberg, Washington, DC, USA, 2010.
  16. Australian Transport Safety Bureau (ATSB), “Uncontained engine failure and air turn-back near San Francisco airport,” in ATSB Transport Safety Aviation Occurrence Investigation, 2010. View at Google Scholar
  17. N. Z. Gebraeel, M. A. Lawley, R. Li, and J. K. Ryan, “Residual-life distributions from component degradation signals: a Bayesian approach,” IIE Transactions, vol. 37, no. 6, pp. 543–557, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. N. Gebraeel, “Sensory-updated residual life distributions for components with exponential degradation patterns,” IEEE Transactions on Automation Science and Engineering, vol. 3, no. 4, pp. 382–393, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. X.-S. Si, W. Wang, C.-H. Hu, and D.-H. Zhou, “Remaining useful life estimation—a review on the statistical data driven approaches,” European Journal of Operational Research, vol. 213, no. 1, pp. 1–14, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  20. J. Z. Sikorska, M. Hodkiewicz, and L. Ma, “Prognostic modelling options for remaining useful life estimation by industry,” Mechanical Systems and Signal Processing, vol. 25, no. 5, pp. 1803–1836, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. M. M. Gaber, A. Zaslavsky, and S. Krishnaswamy, “Mining data streams: a review,” SIGMOD Record, vol. 34, no. 2, pp. 18–26, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. A. Famili, W.-M. Shen, R. Weber, and E. Simoudis, “Data preprocessing and intelligent data analysis,” Intelligent Data Analysis, vol. 1, no. 1, pp. 3–23, 1997. View at Publisher · View at Google Scholar · View at Scopus
  23. Ø. D. Trier, A. K. Jain, and T. Taxt, “Feature extraction methods for character recognition—a survey,” Pattern Recognition, vol. 29, no. 4, pp. 641–662, 1996. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Lei, Z. He, and Y. Zi, “Application of an intelligent classification method to mechanical fault diagnosis,” Expert Systems with Applications, vol. 36, no. 6, pp. 9941–9948, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. Lei and M. J. Zuo, “Gear crack level identification based on weighted K nearest neighbor classification algorithm,” Mechanical Systems and Signal Processing, vol. 23, no. 5, pp. 1535–1547, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. W. Wang, P. Jones, and D. Partridge, “A comparative study of feature-salience ranking techniques,” Neural Computation, vol. 13, no. 7, pp. 1603–1623, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  27. R. Battiti, “Using mutual information for selecting features in supervised neural net learning,” IEEE Transactions on Neural Networks, vol. 5, no. 4, pp. 537–550, 1994. View at Publisher · View at Google Scholar · View at Scopus
  28. S. Wu and T. W. S. Chow, “Induction machine fault detection using SOM-based RBF neural networks,” IEEE Transactions on Industrial Electronics, vol. 51, no. 1, pp. 183–194, 2004. View at Publisher · View at Google Scholar · View at Scopus
  29. P. Mitra, C. A. Murthy, and S. K. Pal, “Density-based multiscale data condensation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 734–747, 2002. View at Publisher · View at Google Scholar · View at Scopus
  30. A. K. S. Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mechanical Systems and Signal Processing, vol. 20, no. 7, pp. 1483–1510, 2006. View at Publisher · View at Google Scholar · View at Scopus
  31. M. Lebold, K. McClintic, R. Campbell, C. Byington, and K. Maynard, “Review of vibration analysis methods for gearbox diagnostics and prognostics,” in Proceedings of the 54th Meeting of the Society for Machinery Failure Prevention Technology, pp. 623–634, Virginia Beach, Va, USA, 2000.
  32. P. D. Samuel and D. J. Pines, “A review of vibration-based techniques for helicopter transmission diagnostics,” Journal of Sound and Vibration, vol. 282, no. 1-2, pp. 475–508, 2005. View at Publisher · View at Google Scholar · View at Scopus
  33. E. Bechhoefer and M. Kingsley, “A review of time synchronous average algorithms,” in Proceedings of the Annual Conference of the Prognostics and Health Management Society, 2009.
  34. S. Braun, “The synchronous (time domain) average revisited,” Mechanical Systems and Signal Processing, vol. 25, no. 4, pp. 1087–1102, 2011. View at Publisher · View at Google Scholar · View at Scopus
  35. P. J. Brockwell and R. A. Davis, Time Series: Theory and Methods, Springer, New York, NY, USA, 2009.
  36. M. Yang and V. Makis, “ARX model-based gearbox fault detection and localization under varying load conditions,” Journal of Sound and Vibration, vol. 329, no. 24, pp. 5209–5221, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. H. Li, H. Zheng, and L. Tang, “Gear fault diagnosis based on order cepstrum analysis,” Journal of Vibration and Shock, vol. 25, no. 5, pp. 65–68, 2006. View at Google Scholar · View at Scopus
  38. C. L. Nikias and J. M. Mendel, “Signal processing with higher-order spectra,” IEEE Signal Processing Magazine, vol. 10, no. 3, pp. 10–37, 1993. View at Publisher · View at Google Scholar
  39. L. Qu, Y. Chen, and J. Liu, “The holospectrum: a new FFT based rotor diagnostic method,” in Proceedings of the 1st International Machinery Monitoring and Diagnostics Conference, pp. 196–201, Las Vegas, Nev, USA, 1989.
  40. K. Gröchenig, Foundations of Time-Frequency Analysis, Birkhäuser, Boston, Mass, USA, 2000. View at Publisher · View at Google Scholar · View at MathSciNet
  41. Q. He, Y. Liu, Q. Long, and J. Wang, “Time-frequency manifold as a signature for machine health diagnosis,” IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 5, pp. 1218–1230, 2012. View at Publisher · View at Google Scholar · View at Scopus
  42. M. R. Portnoff, “Time-frequency representation of digital signals and systems based on short-time fourier analysis,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 28, no. 1, pp. 55–59, 1980. View at Publisher · View at Google Scholar · View at Scopus
  43. J. Jin and J. Shi, “Feature-preserving data compression of stamping tonnage information using wavelets,” Technometrics, vol. 41, no. 4, pp. 327–339, 1999. View at Publisher · View at Google Scholar · View at Scopus
  44. J. Jin and J. Shi, “Automatic feature extraction of waveform signals for in-process diagnostic performance improvement,” Journal of Intelligent Manufacturing, vol. 12, no. 3, pp. 257–268, 2001. View at Publisher · View at Google Scholar · View at Scopus
  45. H. Zheng, Z. Li, and X. Chen, “Gear fault diagnosis based on continuous wavelet transform,” Mechanical Systems and Signal Processing, vol. 16, no. 2-3, pp. 447–457, 2002. View at Publisher · View at Google Scholar · View at Scopus
  46. N. G. Nikolaou and I. A. Antoniadis, “Rolling element bearing fault diagnosis using wavelet packets,” NDT and E International, vol. 35, no. 3, pp. 197–205, 2002. View at Publisher · View at Google Scholar · View at Scopus
  47. J. Lin and L. Qu, “Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis,” Journal of Sound and Vibration, vol. 234, no. 1, pp. 135–148, 2000. View at Publisher · View at Google Scholar · View at Scopus
  48. Z. K. Peng and F. L. Chu, “Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography,” Mechanical Systems and Signal Processing, vol. 18, no. 2, pp. 199–221, 2004. View at Publisher · View at Google Scholar · View at Scopus
  49. F. Léonard, “Phase spectrogram and frequency spectrogram as new diagnostic tools,” Mechanical Systems and Signal Processing, vol. 21, no. 1, pp. 125–137, 2007. View at Publisher · View at Google Scholar · View at Scopus
  50. Q. Meng and L. Qu, “Rotating machinery fault diagnosis using Wigner distribution,” Mechanical Systems and Signal Processing, vol. 5, no. 3, pp. 155–166, 1991. View at Publisher · View at Google Scholar · View at Scopus
  51. W. J. Staszewski, K. Worden, and G. R. Tomlinson, “Time-frequency analysis in gearbox fault detection using the Wigner-Ville distribution and pattern recognition,” Mechanical Systems and Signal Processing, vol. 11, no. 5, pp. 673–692, 1997. View at Publisher · View at Google Scholar · View at Scopus
  52. N. Baydar and A. Ball, “A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution,” Mechanical Systems and Signal Processing, vol. 15, no. 6, pp. 1091–1107, 2001. View at Publisher · View at Google Scholar · View at Scopus
  53. S. U. Lee, D. Robb, and C. Besant, “The directional Choi-Williams distribution for the analysis of rotor-vibration signals,” Mechanical Systems and Signal Processing, vol. 15, no. 4, pp. 789–811, 2001. View at Publisher · View at Google Scholar · View at Scopus
  54. Y. Ding, J. Shi, and D. Ceglarek, “Diagnosability analysis of multi-station manufacturing processes,” ASME Transactions Journal of Dynamic Systems Measurement and Control, vol. 124, no. 1, pp. 1–13, 2002. View at Publisher · View at Google Scholar · View at Scopus
  55. S. Zhou, Y. Ding, Y. Chen, and J. Shi, “Diagnosability study of multistage manufacturing processes based on linear mixed-effects models,” Technometrics, vol. 45, no. 4, pp. 312–325, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  56. M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, and D. C. Teneketzis, “Failure diagnosis using discrete-event models,” IEEE Transactions on Control Systems Technology, vol. 4, no. 2, pp. 105–124, 1996. View at Publisher · View at Google Scholar · View at Scopus
  57. A. Benveniste, E. Fabre, S. Haar, and C. Jard, “Diagnosis of asynchronous discrete-event systems: a net unfolding approach,” IEEE Transactions on Automatic Control, vol. 48, no. 5, pp. 714–727, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  58. M. Steinder and A. S. Sethi, “Probabilistic fault localization in communication systems using belief networks,” IEEE/ACM Transactions on Networking, vol. 12, no. 5, pp. 809–822, 2004. View at Publisher · View at Google Scholar · View at Scopus
  59. N. Dawes, J. Altoft, and B. Pagurek, “Network diagnosis by reasoning in uncertain nested evidence spaces,” IEEE Transactions on Communications, vol. 43, no. 2, pp. 466–476, 1995. View at Publisher · View at Google Scholar · View at Scopus
  60. J. Liu, D. Djurdjanovic, K. A. Marko, and J. Ni, “A divide and conquer approach to anomaly detection, localization and diagnosis,” Mechanical Systems and Signal Processing, vol. 23, no. 8, pp. 2488–2499, 2009. View at Publisher · View at Google Scholar · View at Scopus
  61. A. Widodo and B. S. Yang, “Support vector machine in machine condition monitoring and fault diagnosis,” Mechanical Systems and Signal Processing, vol. 21, no. 6, pp. 2560–2574, 2007. View at Publisher · View at Google Scholar · View at Scopus
  62. R. Hao, Z. Peng, Z. Feng, and F. Chu, “Application of support vector machine based on pattern spectrum entropy in fault diagnostics of rolling element bearings,” Measurement Science and Technology, vol. 22, no. 4, Article ID 045708, 2011. View at Publisher · View at Google Scholar · View at Scopus
  63. R. Casimir, E. Boutleux, G. Clerc, and A. Yahoui, “The use of features selection and nearest neighbors rule for faults diagnostic in induction motors,” Engineering Applications of Artificial Intelligence, vol. 19, no. 2, pp. 169–177, 2006. View at Publisher · View at Google Scholar · View at Scopus
  64. V. T. Tran, B.-S. Yang, M.-S. Oh, and A. C. C. Tan, “Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference,” Expert Systems with Applications, vol. 36, no. 2, pp. 1840–1849, 2009. View at Publisher · View at Google Scholar · View at Scopus
  65. W. Sun, J. Chen, and J. Li, “Decision tree and PCA-based fault diagnosis of rotating machinery,” Mechanical Systems and Signal Processing, vol. 21, no. 3, pp. 1300–1317, 2007. View at Publisher · View at Google Scholar · View at Scopus
  66. Y. Lei, M. J. Zuo, Z. He, and Y. Zi, “A multidimensional hybrid intelligent method for gear fault diagnosis,” Expert Systems with Applications, vol. 37, no. 2, pp. 1419–1430, 2010. View at Publisher · View at Google Scholar · View at Scopus
  67. B. Samanta and K. R. Al-Balushi, “Artificial neural network based fault diagnostics of rolling element bearings using time-domain features,” Mechanical Systems and Signal Processing, vol. 17, no. 2, pp. 317–328, 2003. View at Publisher · View at Google Scholar · View at Scopus
  68. S. Khomfoi and L. M. Tolbert, “Fault diagnostic system for a multilevel inverter using a neural network,” IEEE Transactions on Power Electronics, vol. 22, no. 3, pp. 1062–1069, 2007. View at Publisher · View at Google Scholar · View at Scopus
  69. Z. Li, X. Yan, C. Yuan, J. Zhao, and Z. Peng, “Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks,” Journal of Marine Science and Application, vol. 10, no. 1, pp. 17–24, 2011. View at Publisher · View at Google Scholar · View at Scopus
  70. H. Qiu, J. Lee, J. Lin, and G. Yu, “Robust performance degradation assessment methods for enhanced rolling element bearing prognostics,” Advanced Engineering Informatics, vol. 17, no. 3-4, pp. 127–140, 2003. View at Publisher · View at Google Scholar · View at Scopus
  71. D. Wang, Q. Miao, and R. Kang, “Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition,” Journal of Sound and Vibration, vol. 324, no. 3–5, pp. 1141–1157, 2009. View at Publisher · View at Google Scholar · View at Scopus
  72. Y. Pan, J. Chen, and X. Li, “Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means,” Mechanical Systems and Signal Processing, vol. 24, no. 2, pp. 559–566, 2010. View at Publisher · View at Google Scholar · View at Scopus
  73. C. M. Bishop, Pattern Recognition and Machine Learning, vol. 4, Springer, New York, NY, USA, 2006. View at Publisher · View at Google Scholar · View at MathSciNet
  74. T. Hastie, R. Tibshirani, and J. J. H. Friedman, The Elements of Statistical Learning, Springer, New York, NY, USA, 2001.
  75. S. Kotsiantis, I. Zaharakis, and P. Pintelas, “Supervised machine learning: a review of classification techniques,” Frontiers in Artificial Intelligence and Applications, vol. 160, article 3, 2007. View at Google Scholar
  76. G. A. Whitmore, “Estimating degradation by a wiener diffusion process subject to measurement error,” Lifetime Data Analysis, vol. 1, no. 3, pp. 307–319, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  77. X. Wang, “Wiener processes with random effects for degradation data,” Journal of Multivariate Analysis, vol. 101, no. 2, pp. 340–351, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  78. W. Wang, M. Carr, W. Xu, and K. Kobbacy, “A model for residual life prediction based on Brownian motion with an adaptive drift,” Microelectronics Reliability, vol. 51, no. 2, pp. 285–293, 2011. View at Publisher · View at Google Scholar · View at Scopus
  79. Z. S. Ye, K. L. Tsui, Y. Wang, and M. Pecht, “Degradation data analysis using Wiener processes with measurement errors,” IEEE Transactions on Reliability, vol. 62, no. 4, pp. 772–780, 2013. View at Publisher · View at Google Scholar
  80. Z. Ye, N. Chen, and K.-L. Tsui, “A bayesian approach to condition monitoring with imperfect inspections,” Quality and Reliability Engineering International, 2013. View at Publisher · View at Google Scholar · View at Scopus
  81. Z. Wang, C. Hu, W. Wang, Z. Zhou, and X. Si, “A case study of remaining storage life prediction using stochastic filtering with the influence of condition monitoring,” Reliability Engineering and System Safety, vol. 132, pp. 186–195, 2014. View at Google Scholar
  82. X. S. Si, W. Wang, M. Y. Chen, C. H. Hu, and D. H. Zhou, “A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution,” European Journal of Operational Research, vol. 226, no. 1, pp. 53–66, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  83. X.-S. Si, W. Wang, C.-H. Hu, M.-Y. Chen, and D.-H. Zhou, “A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation,” Mechanical Systems and Signal Processing, vol. 35, no. 1-2, pp. 219–237, 2013. View at Publisher · View at Google Scholar · View at Scopus
  84. N. D. Singpurwalla, “Survival in dynamic environments,” Statistical Science, vol. 10, pp. 86–103, 1995. View at Google Scholar
  85. J. Lawless and M. Crowder, “Covariates and random effects in a gamma process model with application to degradation and failure,” Lifetime Data Analysis, vol. 10, no. 3, pp. 213–227, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  86. Z. S. Ye, M. Xie, L. C. Tang, and N. Chen, “Semiparametric estimation of gamma processes for deteriorating products,” Technometrics, 2014. View at Publisher · View at Google Scholar
  87. J. M. van Noortwijk, “A survey of the application of gamma processes in maintenance,” Reliability Engineering & System Safety, vol. 94, no. 1, pp. 2–21, 2009. View at Publisher · View at Google Scholar · View at Scopus
  88. X. Wang and D. Xu, “An inverse Gaussian process model for degradation data,” Technometrics, vol. 52, no. 2, pp. 188–197, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  89. Z. S. Ye and N. Chen, “The inverse Gaussian process as a degradation model,” Technometrics, vol. 56, no. 3, pp. 302–311, 2014. View at Publisher · View at Google Scholar
  90. A. Ghasemi, S. Yacout, and M.-S. Ouali, “Parameter estimation methods for condition-based maintenance with indirect observations,” IEEE Transactions on Reliability, vol. 59, no. 2, pp. 426–439, 2010. View at Publisher · View at Google Scholar · View at Scopus
  91. W. Wang and A. H. Christer, “Towards a general condition based maintenance model for a stochastic dynamic system,” Journal of the Operational Research Society, vol. 51, no. 2, pp. 145–155, 2000. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  92. Z.-Q. Wang, C.-H. Hu, W. Wang, and X.-S. Si, “An additive wiener process-based prognostic model for hybrid deteriorating systems,” IEEE Transactions on Reliability, vol. 63, no. 1, pp. 208–222, 2014. View at Publisher · View at Google Scholar · View at Scopus
  93. W. Q. Meeker and L. A. Escobar, Statistical Methods for Reliability Data, vol. 78, Wiley, New York, NY, USA, 1998.
  94. C. J. Lu and W. Q. Meeker, “Using degradation measures to estimate a time-to-failure distribution,” Technometrics, vol. 35, no. 2, pp. 161–174, 1993. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  95. K. Yang and G. Yang, “Degradation reliability assessment using severe critical values,” International Journal of Reliability, Quality and Safety Engineering, vol. 5, no. 1, pp. 85–95, 1998. View at Publisher · View at Google Scholar · View at Scopus
  96. K. Yang and A. Jeang, “Statistical surface roughness checking procedure based on a cutting tool wear model,” Journal of Manufacturing Systems, vol. 13, no. 1, pp. 1–8, 1994. View at Publisher · View at Google Scholar · View at Scopus
  97. S.-T. Tseng, M. Hamada, and C.-H. Chiao, “Using degradation data to improve fluorescent lamp reliability,” Journal of Quality Technology, vol. 27, no. 4, pp. 363–369, 1995. View at Google Scholar · View at Scopus
  98. K. B. Goode, B. J. Roylance, and J. Moore, “Development of predictive model for monitoring condition of hot strip mill,” Ironmaking and Steelmaking, vol. 25, no. 1, pp. 42–46, 1998. View at Google Scholar · View at Scopus
  99. S. Chakraborty, N. Gebraeel, M. Lawley, and H. Wan, “Residual-life estimation for components with non-symmetric priors,” IIE Transactions, vol. 41, no. 4, pp. 372–387, 2009. View at Publisher · View at Google Scholar · View at Scopus
  100. Z. Xu and D. Zhou, “A degradation measurements based real-time reliability prediction method,” in Proceedings of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS '06), pp. 950–955, Beijing, China, September 2006. View at Publisher · View at Google Scholar · View at Scopus
  101. N. Chen and K. L. Tsui, “Condition monitoring and remaining useful life prediction using degradation signals: Revisited,” IIE Transactions, vol. 45, no. 9, pp. 939–952, 2013. View at Publisher · View at Google Scholar · View at Scopus
  102. N. Z. Gebraeel and M. A. Lawley, “A neural network degradation model for computing and updating residual life distributions,” IEEE Transactions on Automation Science and Engineering, vol. 5, no. 1, pp. 154–163, 2008. View at Publisher · View at Google Scholar · View at Scopus
  103. D. R. Cox, “Regression models and life-tables,” Journal of the Royal Statistical Society. Series B. Methodological, vol. 34, pp. 187–220, 1972. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  104. H. Liao, W. Zhao, and H. Guo, “Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model,” in Proceedings of the Annual Reliability and Maintainability Symposium (RAMS '06), pp. 127–132, Newport Beach, Calif, USA, January 2006. View at Publisher · View at Google Scholar · View at Scopus
  105. Q. Zhou, J. Son, S. Zhou, X. Mao, and M. Salman, “Remaining useful life prediction of individual units subject to hard failure,” IIE Transactions, vol. 46, no. 10, pp. 1017–1030, 2014. View at Publisher · View at Google Scholar
  106. J. L. Bogdanoff and F. Kozin, Probabilistic Models of Cumulative Damage, John Wiley & Sons, New York, NY, USA, 1985.
  107. M.-L. T. Lee and G. A. Whitmore, “Threshold regression for survival analysis: modeling event times by a stochastic process reaching a boundary,” Statistical Science, vol. 21, no. 4, pp. 501–513, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  108. M. L. T. Lee and G. A. Whitmore, “Proportional hazards and threshold regression: their theoretical and practical connections,” Lifetime Data Analysis, vol. 16, no. 2, pp. 196–214, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  109. R. Ahmad and S. Kamaruddin, “An overview of time-based and condition-based maintenance in industrial application,” Computers & Industrial Engineering, vol. 63, no. 1, pp. 135–149, 2012. View at Publisher · View at Google Scholar · View at Scopus
  110. H. Wang, “A survey of maintenance policies of deteriorating systems,” European Journal of Operational Research, vol. 139, no. 3, pp. 469–489, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  111. M. Woodyard, “GM monitor may add miles between oil changes,” Automotive News, vol. 73, p. 28, 1998. View at Google Scholar
  112. V. Makis and A. K. S. Jardine, “Optimal replacement in the proportional hazards model,” INFOR, vol. 30, pp. 172–183, 1992. View at Google Scholar
  113. B. Bergman, “Optimal replacement under a general failure model,” Advances in Applied Probability, vol. 10, no. 2, pp. 431–451, 1978. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  114. W. Wang, “A model to determine the optimal critical level and the monitoring intervals in condition-based maintenance,” International Journal of Production Research, vol. 38, no. 6, pp. 1425–1436, 2000. View at Publisher · View at Google Scholar · View at Scopus
  115. A. Grall, C. Bérenguer, and L. Dieulle, “A condition-based maintenance policy for stochastically deteriorating systems,” Reliability Engineering & System Safety, vol. 76, no. 2, pp. 167–180, 2002. View at Publisher · View at Google Scholar · View at Scopus
  116. H. R. Golmakani and F. Fattahipour, “Optimal replacement policy and inspection interval for condition-based maintenance,” International Journal of Production Research, vol. 49, no. 17, pp. 5153–5167, 2011. View at Publisher · View at Google Scholar · View at Scopus
  117. K. T. Huynh, A. Barros, C. Bérenguer, and I. T. Castro, “A periodic inspection and replacement policy for systems subject to competing failure modes due to degradation and traumatic events,” Reliability Engineering and System Safety, vol. 96, no. 4, pp. 497–508, 2011. View at Publisher · View at Google Scholar · View at Scopus
  118. L. Dieulle, C. Bérenguer, A. Grall, and M. Roussignol, “Sequential condition-based maintenance scheduling for a deteriorating system,” European Journal of Operational Research, vol. 150, no. 2, pp. 451–461, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  119. W. Wang, “Modelling condition monitoring intervals: a hybrid of simulation and analytical approaches,” Journal of the Operational Research Society, vol. 54, no. 3, pp. 273–282, 2003. View at Publisher · View at Google Scholar · View at Scopus
  120. R. J. Ferreira, A. T. de Almeida, and C. A. Cavalcante, “A multi-criteria decision model to determine inspection intervals of condition monitoring based on delay time analysis,” Reliability Engineering & System Safety, vol. 94, no. 5, pp. 905–912, 2009. View at Publisher · View at Google Scholar · View at Scopus
  121. B. Castanier, C. Bérenguer, and A. Grall, “A sequential condition-based repair/replacement policy with non-periodic inspections for a system subject to continuous wear,” Applied Stochastic Models in Business and Industry, vol. 19, no. 4, pp. 327–347, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  122. H. Liao, E. A. Elsayed, and L.-Y. Chan, “Maintenance of continuously monitored degrading systems,” European Journal of Operational Research, vol. 175, no. 2, pp. 821–835, 2006. View at Publisher · View at Google Scholar · View at Scopus
  123. J. Barata, C. Soares, M. Marseguerra, and E. Zio, “Simulation modelling of repairable multi-component deteriorating systems for “on condition” maintenance optimisation,” Reliability Engineering & System Safety, vol. 76, no. 3, pp. 255–264, 2002. View at Publisher · View at Google Scholar · View at Scopus
  124. M.-Y. You, L. Li, G. Meng, and J. Ni, “Cost-effective updated sequential predictive maintenance policy for continuously monitored degrading systems,” IEEE Transactions on Automation Science and Engineering, vol. 7, no. 2, pp. 257–265, 2010. View at Publisher · View at Google Scholar · View at Scopus
  125. M.-Y. You, F. Liu, W. Wang, and G. Meng, “Statistically planned and individually improved predictive maintenance management for continuously monitored degrading systems,” IEEE Transactions on Reliability, vol. 59, no. 4, pp. 744–753, 2010. View at Publisher · View at Google Scholar · View at Scopus
  126. Z. Tian, T. Jin, B. Wu, and F. Ding, “Condition based maintenance optimization for wind power generation systems under continuous monitoring,” Renewable Energy, vol. 36, no. 5, pp. 1502–1509, 2011. View at Publisher · View at Google Scholar · View at Scopus
  127. L. Li, M. You, and J. Ni, “Reliability-based dynamic maintenance threshold for failure prevention of continuously monitored degrading systems,” Journal of Manufacturing Science and Engineering, vol. 131, no. 3, Article ID 031010, 2009. View at Publisher · View at Google Scholar · View at Scopus
  128. E. A. Elsayed and H. Zhang, “Optimum threshold level of degraded structures based on sensors data,” in Proceedings of the 12th ISSAT International Conference on Reliability and Quality in Design, pp. 187–191, August 2006. View at Scopus
  129. D. I. Cho and M. Parlar, “A survey of maintenance models for multi-unit systems,” European Journal of Operational Research, vol. 51, no. 1, pp. 1–23, 1991. View at Publisher · View at Google Scholar · View at Scopus
  130. R. Nicola and R. Dekker, “Optimal maintenance of multi-component systems: a review,” in Complex System Maintenance Handbook, D. N. P. Murthy and A. K. S. Kobbacy, Eds., Springer, Amsterdam, The Netherlands, 2008. View at Google Scholar
  131. Z. Tian and H. Liao, “Condition based maintenance optimization for multi-component systems using proportional hazards model,” Reliability Engineering and System Safety, vol. 96, no. 5, pp. 581–589, 2011. View at Publisher · View at Google Scholar · View at Scopus
  132. Q. Zhu, H. Peng, and G. J. van Houtum, “A condition-based maintenance policy for multi-component systems with a high maintenance setup cost,” BETA Working Paper 400, Eindhoven University of Technology, 2012. View at Google Scholar
  133. Y. Hai, K. Tsui, and M. Zuo, “Gear crack level classification based on multinomial logit model and cumulative link model,” in Proceedings of the 3rd Annual IEEE Prognostics and System Health Management Conference (PHM '12), pp. 1–6, IEEE, May 2012. View at Publisher · View at Google Scholar · View at Scopus
  134. Y. Xing, E. Ma, K. Tsui, and M. Pecht, “An ensemble model for predicting the remaining useful performance of lithium-ion batteries,” Microelectronics Reliability, vol. 53, no. 6, pp. 811–820, 2013. View at Publisher · View at Google Scholar
  135. J. Lee, R. Abujamra, A. K. Jardine, D. Lin, and D. Banjevic, “An integrated platform for diagnostics, prognostics and maintenance optimization,” in Proceedings of the Intelligent Maintenance Systems Conference, pp. 15–27, 2004.