644
Views
26
CrossRef citations to date
0
Altmetric
Original Articles

Residual life estimation based on nonlinear-multivariate Wiener processes

, &
Pages 1742-1764 | Received 30 Jan 2013, Accepted 24 Feb 2014, Published online: 18 Mar 2014

References

  • Pecht M, Jaai R. A prognostics and health management roadmap for information and electrics-rich system. Microelectron Reliab. 2010;50:317–323. doi: 10.1016/j.microrel.2010.01.006
  • Jardine AKS, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process. 2006;20:1483–1510. doi: 10.1016/j.ymssp.2005.09.012
  • Si XS, Wang WB, Hu CH, Zhou DH. Remaining useful life estimation – a review on the statistical data driven approaches. Eur J Oper Res. 2011;213:1–14. doi: 10.1016/j.ejor.2010.11.018
  • Gebraeel NZ, Lawley MA, Li R, Ryan JK. Residual-life distributions from component degradation signals: a Bayesian approach. IIE Trans. 2005;37:543–557. doi: 10.1080/07408170590929018
  • Wang WB, Carr M, Xu WJ, Kobbacy AK. A model for residual life prediction based on brownian motion with an adaptive drift. Microelectron Reliab. 2010;51:285–293. doi: 10.1016/j.microrel.2010.09.013
  • Noortwijk JM. A survey of the application of gamma processes in maintenance. Reliab Eng Syst Safety. 2009;94:2–21. doi: 10.1016/j.ress.2007.03.019
  • Kharoufeh JP. Explicit results for wear processes in a Markovian environment. Oper Res Lett. 2003;31:237–244. doi: 10.1016/S0167-6377(02)00229-8
  • Wang W. A model to predict the residual life of rolling element bearings given monitored condition information to date. IMA J Manage Math. 2002;13:3–16. doi: 10.1093/imaman/13.1.3
  • Cox DR. Regression models and life-tables. J R Stat SocB, Methodological. 1972;34:187–220.
  • Zhou Z. Hu C. Xu D. Chen M, Zhou D. A model for real-time failure prognosis based on hidden Markov model and belief rule base. Eur J Oper Res. 2010;207:269–283. doi: 10.1016/j.ejor.2010.03.032
  • Kahle W, Wendt H. On a cumulative damage process and resulting first passages times. Appl Stoch Models Bus Ind. 2004;20:17–26. doi: 10.1002/asmb.511
  • Lee MLT, Whitmore GA. Threshold regression for survival analysis: modeling event times by a stochastic process reaching a boundary. Stat Sci. 2006;21:501–513. doi: 10.1214/088342306000000330
  • Gebraeel NZ. Sensory-updated residual life distributions for components with exponential degradation patterns. IEEE Trans Autom Sci Eng. 2006;3:382–393. doi: 10.1109/TASE.2006.876609
  • Gebraeel NZ, Elwany A, Pan J. Residual life predictions in the absence of prior degradation knowledge. IEEE Transactions on Reliability. 2009;58:106–117. doi: 10.1109/TR.2008.2011659
  • Elwang A, Gebraeel NZ. Real-time estimation of mean remaining life using sensor-based degradation models. J Manuf Sci Eng. 2009;131:0510051–0510059.
  • Elwany AH, Gebraeel NZ. Sensor-driven prognostic models for equipment replacement and spare parts inventory. IIE Trans. 2008;40:629–639. doi: 10.1080/07408170701730818
  • Kaiser KA, Gebraeel NZ. Predictive maintenance management using sensor-based degradation models. IEEE Trans SystMan Cybern – A: Syst Humans. 2009;39:840–849. doi: 10.1109/TSMCA.2009.2016429
  • Lu JC, Meeker WQ. Using degradation measures to estimate a time-to-failure distribution. Technometrics. 1993;35:161–174. doi: 10.1080/00401706.1993.10485038
  • Bae S, Kvam P. A nonlinear random-coefficients model for degradation. Technometrics. 2004;46:460–469. doi: 10.1198/004017004000000464
  • Yuan X, Pandey M. A nonlinear mixed-effects model for degradation data obtained from in-service inspections. Reliab Eng Syst Safety. 2009;94:509–519. doi: 10.1016/j.ress.2008.06.013
  • Pandey MD, Yuan XX, Noortwijk JM. The influence of temporal uncertainty of deterioration on life-cycle management of structures. Struct Infrastruct Eng. 2009;5:145–156. doi: 10.1080/15732470601012154
  • Peng CY, Tseng ST. Mis-specification analysis of linear degradation models. IEEE Trans Reliab. 2009;58:444–455. doi: 10.1109/TR.2009.2026784
  • Wang X. Wiener processes with random effects for degradation data. Journal of Multivariate Analysis. 2010;101:340–351. doi: 10.1016/j.jmva.2008.12.007
  • Tsai CC, Tseng ST, Balakrishnan N. Mis-specification analyses of gamma and Wiener degradation processes. J Stat Plan Inference. 2011;141:3725–3735. doi: 10.1016/j.jspi.2011.06.008
  • Si XS, Wang W, Hu CH, Zhou DH, Pecht MG. Remaining useful life estimation based on a nonlinear diffusion degradation process. IEEE Trans Reliab. 2012;61:50–67. doi: 10.1109/TR.2011.2182221
  • Sari JK, Newby MJ, Brombacher AC, Tang LC. Bivariate constant stress degradation model: led lighting system reliability estimation with two-stage modelling. Qual Reliab Eng Int. 2009;25:1067–1084. doi: 10.1002/qre.1022
  • Pan Z, Balakrishnan N, Sun Q, Zhou J. Bivariate degradation analysis of products based on Wiener processes and copulas. J Stat Comput Simul. 2012;1:1–14.
  • Crk V. Reliability assessment from degradation data. The Annual Reliability and Maintainability Symposium-Product Quality & Integrity, RAMS, Los Angeles; 2000. p. 155–161.
  • Huang W, Askin RG. Reliability analysis of electronic devices with multiple competing failure modes involving performance aging degradation. Qual Reliab Eng Int. 2003;19:241–254. doi: 10.1002/qre.524
  • Wang P, Coit DW. Reliability prediction based on degradation modeling for systems with multiple degradation measures. The Annual Reliability and Maintainability Symposium-Product Quality & Integrity. RAMS, Los Angeles; 2004. p. 302–307.
  • Sari JK. Multivariate degradation modeling and its application to reliability testing. Singapore: National University of Singapore; 2007.
  • Pan Z, Balakrishnan N, Sun Q. Bivariate constant-stress accelerated degradation model and inference. Commun Stat – Simul Comput. 2011;40:259–269.
  • Wang Y, Pham H. Modeling the dependent competing risks with multiple degradation processes and random shock using time-varying copulas. IEEE Trans Reliab. 2012;61:13–22. doi: 10.1109/TR.2011.2170253
  • Zhou DH, Frank PM. Strong tracking filtering of nonlinear time-varying stochastic systems with coloured noise: Application to parameter estimation and empirical robustness analysis. Int J Control. 1996;65:295–307. doi: 10.1080/00207179608921698
  • Nelsen RB. An introduction to copulas. 2nd ed. New York: Springer Science; 2006.
  • Fujikoshi Y, Ulyanov V, Shimizu R. Multivariate statistics: high-dimensional and large-sample approximations. Hoboken, NJ: Wiley; 2010.
  • Meeker WQ, Escobar LA. Statistical methods for reliability data. New York: Wiley; 1998.
  • Ellingwood BR, Mori Y. Probabilistic methods for condition assessment and life prediction of concrete structures in nuclear power plants. Nuclear Eng Design. 1993;142:155–166. doi: 10.1016/0029-5493(93)90199-J
  • Cinlar E, Bazant ZP, Osman E. Stochastic process for extrapolating concrete creep. J Eng Mech Div. 1977;103:1069–1088.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.