Prognostics based on the generalized diffusion process with parameters updated by a sequential Bayesian method (original) (raw)
References
Pecht M. Prognostics and Health Management of Electronics. Hoboken: Wiley, 2008 Book Google Scholar
Jardine A K S, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process, 2006, 20: 1483–1510 Article Google Scholar
Lee J, Wu F, Zhao W, et al. Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications. Mech Syst Signal Process, 2014, 42: 314–334 Article Google Scholar
Tsui K L, Chen N, Zhou Q, et al. Prognostics and health management: a review on data driven approaches. Math Problems Eng, 2015, 2015: 1–17 Article Google Scholar
Lei Y, Li N, Guo L, et al. Machinery health prognostics: a systematic review from data acquisition to RUL prediction. Mech Syst Signal Process, 2018, 104: 799–834 Article Google Scholar
Si X S, Li T M, Zhang Q. A general stochastic degradation modeling approach for prognostics of degrading systems with surviving and uncertain measurements. IEEE Trans Rel, 2019, 68: 1080–1100 Article Google Scholar
Zhai Q, Ye Z S. RUL prediction of deteriorating products using an adaptive Wiener process model. IEEE Trans Ind Inf, 2017, 13: 2911–2921 Article Google Scholar
Lall P, Lowe R, Goebel K. Prognostics health management of electronic systems under mechanical shock and vibration using kalman filter models and metrics. IEEE Trans Ind Electron, 2012, 59: 4301–4314 Article Google Scholar
Si X S, Li T M, Zhang Q, et al. Prognostics for linear stochastic degrading systems with survival measurements. IEEE Trans Ind Electron, 2020, 67: 3202–3215 Article Google Scholar
Xi X P, Chen M Y, Zhou D H. Remaining useful life prediction for multi-component systems with hidden dependencies. Sci China Inf Sci, 2019, 62: 022202 ArticleMathSciNet Google Scholar
Yu Y, Si X S, Hu C H, et al. Online remaining-useful-life estimation with a Bayesian-updated expectation-conditional-maximization algorithm and a modified Bayesian-model-averaging method. Sci China Inf Sci, 2021, 64: 112205 ArticleMathSciNet Google Scholar
Jin X, Sun Y, Que Z, et al. Anomaly detection and fault prognosis for bearings. IEEE Trans Instrum Meas, 2016, 65: 2046–2054 Article Google Scholar
Si X S, Li T M, Zhang Q. Optimal replacement of degrading components: a control-limit policy. Sci China Inf Sci, 2021, 64: 209205 ArticleMathSciNet Google Scholar
Si X S, Hu C H, Li T M, et al. A joint order-replacement policy for deteriorating components with reliability constraint. Sci China Inf Sci, 2021, 64: 189203 Article Google Scholar
Verbert K, de Schutter B, Babuska R. A multiple-model reliability prediction approach for condition-based maintenance. IEEE Trans Rel, 2018, 67: 1364–1376 Article Google Scholar
Hu C H, Pei H, Si X S, et al. A prognostic model based on DBN and diffusion process for degrading bearing. IEEE Trans Ind Electron, 2020, 67: 8767–8777 Article Google Scholar
Gebraeel N, Lawley M, Liu R, et al. Residual life predictions from vibration-based degradation signals: a neural network approach. IEEE Trans Ind Electron, 2004, 51: 694–700 Article Google Scholar
García-Nieto P J, García-Gonzalo E, Sánchez-Lasheras F, et al. Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliability Eng Syst Saf, 2015, 138: 219–231 Article Google Scholar
Guo L, Lei Y, Li N, et al. Machinery health indicator construction based on convolutional neural networks considering trend burr. Neurocomputing, 2018, 292: 142–150 Article Google Scholar
Deutsch J, He D. Using deep learning-based approach to predict remaining useful life of rotating components. IEEE Trans Syst Man Cybern Syst, 2018, 48: 11–20 Article Google Scholar
Zhang J X, Hu C H, He X, et al. A novel lifetime estimation method for two-phase degrading systems. IEEE Trans Rel, 2019, 68: 689–709 Article Google Scholar
Wang D, Zhao Y, Yang F, et al. Nonlinear-drifted Brownian motion with multiple hidden states for remaining useful life prediction of rechargeable batteries. Mech Syst Signal Process, 2017, 93: 531–544 Article Google Scholar
Ling M H, Ng H K T, Tsui K L. Bayesian and likelihood inferences on remaining useful life in two-phase degradation models under gamma process. Reliability Eng Syst Saf, 2019, 184: 77–85 Article Google Scholar
Ye Z S, Chen N. The inverse Gaussian process as a degradation model. Technometrics, 2014, 56: 302–311 ArticleMathSciNet Google Scholar
Doksum K A, Hoyland A. Models for variable-stress accelerated life testing experiments based on Wiener processes and the inverse gaussian distribution. Theor Probab Appl, 1993, 37: 137–139 ArticleMathSciNetMATH Google Scholar
Tseng S T, Tang J, Ku I H. Determination of burn-in parameters and residual life for highly reliable products. Naval Res Logistics, 2003, 50: 1–14 ArticleMathSciNetMATH Google Scholar
Tseng S T, Peng C Y. Optimal burn-in policy by using an integrated Wiener process. IIE Trans, 2004, 36: 1161–1170 Article Google Scholar
Elwany A, Gebraeel N. Real-time estimation of mean remaining life using sensor-based degradation models. J Manufacturing Sci Eng, 2009, 131: 051005 Article Google Scholar
Si X S, Wang W, Chen M Y, et al. A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution. Eur J Operational Res, 2013, 226: 53–66 ArticleMathSciNetMATH Google Scholar
Gebraeel N Z, Lawley M A, Li R, et al. Residual-life distributions from component degradation signals: a Bayesian approach. IIE Trans, 2005, 37: 543–557 Article Google Scholar
Whitmore G A, Schenkelberg F. Modeling accelerated degradation data using Wiener diffusion with a time scale transformation. Lifetime Data Anal, 1997, 3: 27–45 ArticleMATH Google Scholar
Si X S, Wang W, Hu C H, et al. Remaining useful life estimation based on a nonlinear diffusion degradation process. IEEE Trans Rel, 2012, 61: 50–67 Article Google Scholar
Si X S, Ren Z Q, Hu X X, et al. A novel degradation modeling and prognostic framework for closed-loop systems with degrading actuator. IEEE Trans Ind Electron, 2020, 67: 9635–9647 Article Google Scholar
Tang S J, Guo X S, Yu C Q, et al. Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors. J Cent South Univ, 2014, 21: 4509–4517 Article Google Scholar
Wang Z Q, Hu C H, Wang W, et al. An additive Wiener process-based prognostic model for hybrid deteriorating systems. IEEE Trans Rel, 2014, 63: 208–222 Article Google Scholar
Zhang Z X, Hu C H, Si X S, et al. Stochastic degradation process modeling and remaining useful life estimation with flexible random-effects. J Franklin Institute, 2017, 354: 2477–2499 ArticleMathSciNetMATH Google Scholar
Wang Y E, Ma W M, Chow T W S, et al. A two-step parametric method for failure prediction in hard disk drives. IEEE Trans Indust Inform, 2014, 10: 419–430 Article Google Scholar
Ebenezer R H P, Susan E, Srinivasan R, et al. Template-based gait authentication through Bayesian thresholding. IEEE/CAA J Autom Sin, 2019, 6: 209–219 Article Google Scholar
Fang H Z, Tian N, Wang Y B, et al. Nonlinear Bayesian estimation: from Kalman filtering to a broader horizon. IEEE/CAA J Autom Sin, 2018, 5: 401–417 ArticleMathSciNet Google Scholar