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An Adaptive Remaining Useful Life Estimation Approach with a Recursive Filter

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Data-Driven Remaining Useful Life Prognosis Techniques

Part of the book series: Springer Series in Reliability Engineering ((RELIABILITY))

Abstract

Enhancing safety, efficiency, availability, and effectiveness of industrial and military systems through prognostics and health management (PHM) paradigm has gained momentum over the last decade (Pecht, Prognostics and health management of electronics, 2008, [1]; Si et al., Eur J Oper Res 213:1–14, 2011, [2]). PHM is a systematic approach that is used to evaluate the reliability of a system in its actual life cycle conditions, predict failure progression, and mitigate operating risks via management actions.

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References

  1. Pecht M (2008) Prognostics and health management of electronics. Wiley, New Jersey

    Book  Google Scholar 

  2. Si XS, Wang W, Hu CH, Zhou DH (2011) Remaining useful life estimation-A review on the statistical data driven approaches. Eur J Oper Res 213:1–14

    Article  MathSciNet  Google Scholar 

  3. Camci F, Chinnam RB (2010) Health state estimation and prognostics in machining processes. IEEE Trans Autom Sci Eng 7:581–597

    Article  Google Scholar 

  4. Carr MJ, Wang W (2010) Modeling failure modes for residual life prediction using stochastic filtering theory. IEEE Trans Reliab 59:346–355

    Article  Google Scholar 

  5. Dong M, He D (2007) Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis. Eur J Oper Res 178:858–878

    Article  MATH  Google Scholar 

  6. Peng Y, Dong M (2011) A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction. Mech Syst Signal Process 25:237–252

    Article  Google Scholar 

  7. Sikorska J, Hodkiewicz M, Ma L (2011) Prognostic modelling options for remaining useful life estimation by industry. Mech Syst Signal Process 25:1803–1836

    Article  Google Scholar 

  8. Mazhar MI, Kara S, Kaebernick H (2007) Remaining life estimation of used components in consumer products Life cycle data analysis by Weibull and artificial neural networks. J Oper Manag 25:1184–1193

    Article  Google Scholar 

  9. Wang W (2007) A two-stage prognosis model in condition based maintenance. Eur J Oper Res 182:1177–1187

    Article  MATH  Google Scholar 

  10. You MY, Li L, Meng G, Ni J (2010) Statistically planned and individual improved predictive maintenance management for continuously monitored degrading systems. IEEE Trans Reliab 59:744–753

    Article  Google Scholar 

  11. Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20:1483–1510

    Article  Google Scholar 

  12. Lee M-LT, Whitmore GA (2006) Threshold regression for survival analysis: modeling event times by a stochastic process reaching a boundary. Stat Sci 21:501–513

    Article  MathSciNet  MATH  Google Scholar 

  13. Escobar LA, Meeker WQ (2006) A review of accelerated test models. Stat Sci 21:552–577

    Article  MathSciNet  MATH  Google Scholar 

  14. Joseph VR, Yu IT (2006) Reliability improvement experiments with degradation data. IEEE Trans Reliab 55:149–157

    Article  Google Scholar 

  15. Lu CJ, Meeker WQ (1993) Using degradation measures to estimate a time-to-failure distribution. Technometrics 35:161–174

    Article  MathSciNet  MATH  Google Scholar 

  16. Park JI, Bae SJ (2010) Direct prediction methods on lifetime distribution of organic light-emitting diodes from accelerated degradation tests. IEEE Trans Reliab 59:74–90

    Article  Google Scholar 

  17. Pandey MD, Yuan X-X, van Noortwijk JM (2009) The influence of temporal uncertainty of deterioration on life-cycle management of structures. Struct Infrastruct Eng 5:145–156

    Article  Google Scholar 

  18. Bloch-Mecier S (2002) A preventive maintenance policy with sequential checking procedure for a Markov deteriorating system. Eur J Oper Res 147:548–576

    Article  MathSciNet  MATH  Google Scholar 

  19. Gebraeel N, Lawley MA, Li R, Ryan JK (2005) Residual-life distributions from component degradation signals: a Bayesian approach. IIE Trans 37:543–557

    Article  Google Scholar 

  20. Delia MC, Rafael PO (2008) A maintenance model with failures and inspection following Markovian arrival processes and two repair modes. Eur J Oper Res 186:694–707

    Article  MathSciNet  MATH  Google Scholar 

  21. Liao HT, Elsayed EA (2006) Reliability inference for field conditions from accelerated degradation testing. Naval Res Logist 53:576–587

    Article  MathSciNet  MATH  Google Scholar 

  22. Barker CT, Newby MJ (2009) Optimal non-periodic inspection for a multivariate degradation model. Reliab Eng Syst Safety 94:33–43

    Article  Google Scholar 

  23. Tseng ST, Tang J, Ku LH (2003) Determination of optimal burn-in parameters and residual life for highly reliable products. Naval Res Logist 50:1–14

    Article  MathSciNet  MATH  Google Scholar 

  24. Whitmore GA, Schenkelberg F (1997) Modelling accelerated degradation data using Wiener diffusion with a time scale transformation. Lifetime Data Anal 3:27–45

    Article  MATH  Google Scholar 

  25. Peng CY, Tseng ST (2009) Mis-specification analysis of linear degradation models. IEEE Trans Reliab 58:444–455

    Article  Google Scholar 

  26. Wang X (2010) Wiener processes with random effects for degradation data. J Multivar Anal 101:340–351

    Article  MathSciNet  MATH  Google Scholar 

  27. Ray A, Tangirala S (1996) Stochastic modeling of fatigue crack dynamics for on-line failure prognostics. IEEE Trans Control Syst Technol 4:443–450

    Article  Google Scholar 

  28. Tseng ST, Peng CY (2004) Optimal burn-in policy by using an integrated Wiener process. IIE Trans 36:1161–1170

    Article  Google Scholar 

  29. Crowder M, Lawless J (2007) On a scheme for predictive maintenance. Eur J Oper Res 176:1713–1722

    Article  MathSciNet  MATH  Google Scholar 

  30. Tang J, Su TS (2008) Estimating failure time distribution and its parameters based on intermediate data from a Wiener degradation model. Naval Res Logist 55:265–276

    Article  MathSciNet  MATH  Google Scholar 

  31. Xu ZG, Ji YD, Zhou DH (2008) Real-time reliability prediction for a dynamic system based on the hidden degradation process identification. IEEE Trans Reliab 57:230–242

    Article  Google Scholar 

  32. Wang W, Carr M, Xu W, Kobbacy AKH (2011) A model for residual life prediction based on Brownian motion with an adaptive drift. Microeletron Reliab 51:285–293

    Article  Google Scholar 

  33. Elwany AH, Gebraeel NZ (2009) Real-time estimation of mean remaining life using sensor-based degradation models. J Manuf Sci Eng 131:1–9(051005)

    Google Scholar 

  34. Christer AH, Wang W (1992) A model of condition monitoring of a production plant. Int J Prod Res 30:2199–2211

    Article  Google Scholar 

  35. Li R, Ryan JK (2011) A Bayesian inventory model using real-time condition monitoring information. Prod Oper Manag 20:754–771

    Article  Google Scholar 

  36. Cox DR, Miller HD (1965) The theory of stochastic processes. Methuen and Company, London

    MATH  Google Scholar 

  37. Montgomery DC (2008) Introduction to statistical quality control, 6th edn. Wiley, New York

    MATH  Google Scholar 

  38. Harvey AC (1989) Forecasting, structural time series models and the Kalman filter. Cambradge University Press, Cambridge

    Google Scholar 

  39. Zhou DH, Frank PM (1996) Strong tracking filtering of nonlinear time-varying stochastic systems with colored noise: application to parameter estimation and empirical robustness analysis. Int J Control 65:295–307

    Article  MATH  Google Scholar 

  40. Zhou DH, Frank PM (1998) Fault diagnostics and fault tolerant control. IEEE Trans Aerosp Electron Syst 34:420–427

    Article  Google Scholar 

  41. Whitmore GA (1986) Normal-Gamma mixture of inverse Gaussian distributions. Scand J Stat 13:211–220

    MathSciNet  MATH  Google Scholar 

  42. Aalen OO (1994) Effects of frailty in survival analysis. Stat Methods Med Res 3:227–243

    Article  Google Scholar 

  43. Derman C, Lieberman GJ, Ross SM (1984) On the use of replacements to extend system life. Oper Res 32:616–627

    Article  MathSciNet  MATH  Google Scholar 

  44. Shechter SM, Bailey MD, Schaefer AJ (2008) Replacing nonidentical vital components to extend system life. Naval Res Logist 55:700–703

    Article  MathSciNet  MATH  Google Scholar 

  45. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39:1–38

    MathSciNet  MATH  Google Scholar 

  46. Kailath T, Sayed A, Hassabi B (2000) Linear estimation. Prentice-Hall, Upper Saddle River

    Google Scholar 

  47. Wu CFJ (1983) On the convergence property of the EM algorithm. Ann Stat 11:95–103

    Article  MATH  Google Scholar 

  48. Dewar M, Scerri K, Kadirkamanathan V (2009) Data-driven spatio-temporal modeling using the integro-difference equation. IEEE Trans Signal Process 57:83–91

    Article  MathSciNet  Google Scholar 

  49. Gibson S, Wills A, Ninness B (2005) Maximum-likelihood parameter estimation of bilinear systems. IEEE Trans Autom Control 50:1581–1596

    Article  MathSciNet  MATH  Google Scholar 

  50. Wills A, Ninness B, Gibson S (2009) Maximum likelihood estimation of state space models from frequency domain data. IEEE Trans Autom Control 54:19–33

    Article  MathSciNet  Google Scholar 

  51. Schön TB, Wills A, Ninness B (2011) System identification of nonlinear state-space models. Automatica 47:39–49

    Article  MathSciNet  MATH  Google Scholar 

  52. Rauch HE, Tung F, Striebel CT (1965) Maximum Likelihood estimates of linear dynamic systems. AIAA J 3:1145–1450

    MathSciNet  Google Scholar 

  53. Jwo DJ, Wang SH (2007) Adaptive fuzzy strong tracking extended Kalman filtering for GPS navigation. IEEE Sensors J 7:778–789

    Article  Google Scholar 

  54. Woodman OJ (2007) An introduction to inertial navigation, Technical report, published by the University of Cambridge Computer Laboratory. http://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-696.html (available via Internet)

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Authors and Affiliations

  1. Department of Automation, Xi’an Institute of High-Technology, Xi’an, Shaanxi, China

    Xiao-Sheng Si, Zheng-Xin Zhang & Chang-Hua Hu

Authors
  1. Xiao-Sheng Si
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  2. Zheng-Xin Zhang
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  3. Chang-Hua Hu
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Correspondence to Xiao-Sheng Si .

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© 2017 National Defense Industry Press and Springer-Verlag GmbH Germany

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Si, XS., Zhang, ZX., Hu, CH. (2017). An Adaptive Remaining Useful Life Estimation Approach with a Recursive Filter. In: Data-Driven Remaining Useful Life Prognosis Techniques. Springer Series in Reliability Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54030-5_4

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  • DOI: https://doi.org/10.1007/978-3-662-54030-5_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-54028-2

  • Online ISBN: 978-3-662-54030-5

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