Modeling left-truncated degradation data using random drift-diffusion Wiener processes

References

• Ahmad, W., Khan, S. A., & Kim, J. M. (2017). A hybrid prognostics technique for rolling element bearings using adaptive predictive models. IEEE Transactions on Industrial Electronics, 65(2), 1577–1584. https://doi.org/10.1109/TIE.2017.2733487
   Web of Science ®Google Scholar
• Bian, L., & Gebraeel, N. (2014b). Stochastic modeling and real-time prognostics for multi-component systems with degradation rate interactions. IIE Transactions: Industrial Engineering Research & Development, 46(5), 470–482. https://doi.org/10.1080/0740817X.2013.812269
   Web of Science ®Google Scholar
• Bian, L., Gebraeel, N., & Kharoufeh, J. P. (2015). Degradation modeling for real-time estimation of residual lifetimes in dynamic environments. IIE Transactions, 47(5), 471–486. https://doi.org/10.1080/0740817X.2014.955153
   Web of Science ®Google Scholar
• De Santis, R. B., Gontijo, T. S., & Costa, M. A. (2022). Condition-based maintenance in hydroelectric plants: A systematic literature review. Proceeding ImechE, Part O: J Risk and Reliability, 236(5), 631–646. doi: https://doi.org/10.1177/1748006X211035623.
   Web of Science ®Google Scholar
• Giorgio, M., Postiglione, F., & Pulcini, G. (2020). Bayesian estimation and prediction for the transformed Wiener degradation process. Appl Stoch Models in Bus Ind, 36(4), 660–678. https://doi.org/10.1002/asmb.2522
   Web of Science ®Google Scholar
• Huynh, K. T. (2019). A hybrid condition-based maintenance model for deteriorating systems subject to nonmemoryless imperfect repairs and perfect replacements. IEEE Transactions on Reliability, 69(2), 781–815. https://doi.org/10.1109/TR.2019.2942019
   Web of Science ®Google Scholar
• Iscioglu, F., & Erem, A. (2021). Reliability analysis of a multi-state system with identical units having two dependent components. Proceedings of the Institution of Mechanical Engineers, Part O: Ournal of Risk and Reliability, 235(2), 241–252. https://doi.org/10.1177/1748006X20957476
   Web of Science ®Google Scholar
• Jiang, W., Ye, Z. S., & Zhao, X. (2020). Reliability estimation from left-truncated and right-censored data using splines. Statistica Sinica, 30(2), 845–875.
   Web of Science ®Google Scholar
• Lee, M. Y., Hu, C. H., & Tang, J. (2017). A two-stage latent variable estimation procedure for time-censored accelerated degradation tests. IEEE Transactions on Reliability, 66(4), 1266–1279. https://doi.org/10.1109/TR.2017.2731680
   Web of Science ®Google Scholar
• Lee, J., Ni, J., Singh, J., Jiang, B., Azamfar, M., & Feng, J. (2020). Intelligent maintenance systems and predictive manufacturing. Journal of Manufacturing Science and Engineering, 142(11), 110805. https://doi.org/10.1115/1.4047856
   Web of Science ®Google Scholar
• Li, N., Lei, Y., Yan, T., Li, N., & Han, T. (2018). A Wiener-process-model-based method for remaining useful life prediction considering unit-to-unit variability. IEEE Transactions on Industrial Electronics, 66(3), 2092–2101. https://doi.org/10.1109/TIE.2018.2838078
   Web of Science ®Google Scholar
• Limon, S., Rezaei, E., & Yadav, O. P. (2020). Designing an accelerated degradation test plan considering the gamma degradation process with multi-stress factors and interaction effects. Quality Technology & Quantitative Management, 17(5), 544–560. https://doi.org/10.1080/16843703.2019.1696010
   Web of Science ®Google Scholar
• Liu, X., Al-Khalifa, K. N., Elsayed, E. A., Coit, D. W., & Hamouda, A. S. (2014). Criticality measures for components with multi-dimensional degradation. IIE Transactions, 46(10), 987–998. https://doi.org/10.1080/0740817X.2013.851433
   Web of Science ®Google Scholar
• Liu, S. W., Wu, C. W., & Wang, Z. H. (2022). An integrated operating mechanism for lot sentencing based on process yield. Quality Technology & Quantitative Management, 19(2), 139–152. https://doi.org/10.1080/16843703.2021.1927295
   Web of Science ®Google Scholar
• Lu, L., Wang, B., Hong, Y., Ye Z. (2020). General path models for degradation data with multiple characteristics and covariates. Technometrics, 62(2), 1–16. https://doi.org/10.1080/00401706.2020.1744909
   Google Scholar
• Ma, Z., Wang, S., Liao, H., & Zhang, C. (2019). Engineering-driven performance degradation analysis of hydraulic piston pump based on the inverse Gaussian process. Quality and Reliability Engineering International, 35(7), 2278–2296. https://doi.org/10.1002/qre.2502
   Web of Science ®Google Scholar
• Meeker, W. Q., & Escobar, L. A. (2014). Statistical methods for reliability data. John Wiley & Sons.
   Google Scholar
• Neumann, T., Dutschk, B., & Schenkendorf, R. (2019). Analyzing uncertainties in model response using the point estimate method: Applications from railway asset management. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 233(5), 761–774. https://doi.org/10.1177/1748006X19825593
   Web of Science ®Google Scholar
• Paroissin, C. (2015). Inference for the Wiener process with random initiation time. IEEE Transactions on Reliability, 65(1), 147–157. https://doi.org/10.1109/TR.2015.2456056
   Web of Science ®Google Scholar
• Peng, C. Y., & Tseng, S. T. (2010). Progressive-stress accelerated degradation test for highly-reliable products. IEEE Transactions on Reliability, 59(1), 30–37. https://doi.org/10.1109/TR.2010.2040769
   Web of Science ®Google Scholar
• Peng, R., Wu, D., Xiao, H., Xing, L., & Gao, K. (2019). Redundancy versus protection for a non-reparable phased-mission system subject to external impacts. Reliability Engineering & System Safety, 191, 106556. https://doi.org/10.1016/j.ress.2019.106556
   Web of Science ®Google Scholar
• Shahraki, A. F., Yadav, O. P., & Liao, H. (2017). A review on degradation modelling and its engineering applications. International Journal of Performability Engineering, 13(3), 299. https://doi.org/10.23940/ijpe.17.03.p6.299314
   Google Scholar
• Shen, L., Wang, Y., Zhai, Q., & Tang, Y. (2018). Degradation modeling using stochastic processes with random initial degradation. IEEE Transactions on Reliability, 68(4), 1320–1329. https://doi.org/10.1109/TR.2018.2885133
   Web of Science ®Google Scholar
• Singleton, R. K., Strangas, E. G., & Aviyente, S. (2016). The use of bearing currents and vibrations in lifetime estimation of bearings. IEEE Transactions on Industrial Informatics, 13(3), 1301–1309. https://doi.org/10.1109/TII.2016.2643693
   Web of Science ®Google Scholar
• Su, C., Li, L., & Wen, Z. (2020). Remaining useful life prediction via a variational autoencoder and a time-window-based sequence neural network. Quality and Reliability Engineering International, 36(5), 1639–1656. https://doi.org/10.1002/qre.2651
   Web of Science ®Google Scholar
• Sun, Q., Ye, Z. S., & Hong, Y. (2020). Statistical modeling of multivariate destructive degradation tests with blocking. Technometrics, 62(4), 536–548. https://doi.org/10.1080/00401706.2019.1668855
   Web of Science ®Google Scholar
• Tsai, C. C., Tseng, S. T., & Balakrishnan, N. (2011). Mis-specification analyses of gamma and Wiener degradation processes. Journal of Statistical Planning and Inference, 141(12), 3725–3735. https://doi.org/10.1016/j.jspi.2011.06.008
   Web of Science ®Google Scholar
• Wang, F. K., & Mamo, T. (2019). Hybrid approach for remaining useful life prediction of ball bearings. Quality and Reliability Engineering International, 35(7), 2494–2505. https://doi.org/10.1002/qre.2538
   Web of Science ®Google Scholar
• Wang, D., & Tsui, K. L. (2018). Brownian motion with adaptive drift for remaining useful life prediction: Revisited. Mechanical Systems and Signal Processing, 99, 691–701. https://doi.org/10.1016/j.ymssp.2017.07.015
   Web of Science ®Google Scholar
• Wang, X., Wang, B. X., Wu, W., & Hong, Y. (2020). Reliability analysis for accelerated degradation data based on the Wiener process with random effects. Quality and Reliability Engineering International, 36(6), 1969–1981. https://doi.org/10.1002/qre.2668
   Web of Science ®Google Scholar
• Wang, L., Yang, Y., Zhu, H., & Liu, G. (2020). Optimal condition-based renewable warranty policy for products with three-stage failure process. Quality Technology & Quantitative Management, 17(2), 216–233. https://doi.org/10.1080/16843703.2019.1584956
   Web of Science ®Google Scholar
• Wang, Z., Zhai, Q., & Chen, P. (2021). Degradation modeling considering unit-to-unit heterogeneity-A general model and comparative study. Reliability Engineering and System Safety, 216, 107897. https://doi.org/10.1016/j.ress.2021.107897
   Web of Science ®Google Scholar
• Weaver, B. P., Meeker, W. Q., Escobar, L. A., & Wendelberger J. (2013). Methods for planning repeated measures degradation studies. Technometrics, 55(2), 122–134. https://doi.org/10.1080/00401706.2012.715838
   Web of Science ®Google Scholar
• Wu, C. W., Shu, M. H., & Wu, N. Y. (2021). Acceptance sampling schemes for two-parameter Lindley lifetime products under a truncated life test. Quality Technology & Quantitative Management, 18(3), 382–395. https://doi.org/10.1080/16843703.2020.1846269
   Web of Science ®Google Scholar
• Xiao, X., & Ye, Z. S. (2016). Optimal design for destructive degradation tests with random initial degradation values using the Wiener process. IEEE Transactions on Reliability, 65(3), 1327–1342. https://doi.org/10.1109/TR.2016.2575442
   Web of Science ®Google Scholar
• Ye, Z. S., Chen, N., & Shen, Y. (2015). A new class of Wiener process models for degradation analysis. Reliability Engineering & System Safety, 139, 58–67. https://doi.org/10.1016/j.ress.2015.02.005
   Web of Science ®Google Scholar
• Ye, Z. S., & Xie, M. (2015). Stochastic modelling and analysis of degradation for highly reliable products. Applied Stochastic Models in Business and Industry, 31(1), 16–32. https://doi.org/10.1002/asmb.2063
   Web of Science ®Google Scholar
• Yuan, X. X., & Pandey, M. D. (2009). A nonlinear mixed-effects model for degradation data obtained from in-service inspections. Reliability Engineering & System Safety, 94(2), 509–519. https://doi.org/10.1016/j.ress.2008.06.013
   Web of Science ®Google Scholar
• Zhai, Q., Chen, P., Hong, L., & Shen, L. (2018). A random-effects Wiener degradation model based on accelerated failure time. Reliability Engineering & System Safety, 180, 94–103. https://doi.org/10.1016/j.ress.2018.07.003
   Web of Science ®Google Scholar
• Zhang, Y. (2022). Stochastic comparisons of conditional residual lifetimes with applications. Quality Technology & Quantitative Management, 1–32. https://doi.org/10.1080/16843703.2022.2136281
   Web of Science ®Google Scholar
• Zhang, J. X., Hu, C. H., Si, X. S., Zhou, Z. -J., & Du, D. -B. (2014). Remaining useful life estimation for systems with time-varying mean and variance of degradation processes. Quality and Reliability Engineering International, 30(6), 829–841. https://doi.org/10.1002/qre.1705
   Web of Science ®Google Scholar
• Zhang, Y., & Liao, H. (2014). Analysis of destructive degradation tests for a product with random degradation initiation time. IEEE Transactions on Reliability, 64(1), 516–527. https://doi.org/10.1109/TR.2014.2336411
   Web of Science ®Google Scholar
• Zhang, Z., Si, X., Hu, C. H., & Lei, Y. (2018). Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods. European Journal of Operational Research, 271(3), 775–796. https://doi.org/10.1016/j.ejor.2018.02.033
   Web of Science ®Google Scholar
• Zhao, X., Huang, X., & Sun, J. (2020). Reliability modeling and maintenance optimization for the two-unit system with preset self-repairing mechanism. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 234(2), 221–234. https://doi.org/10.1177/1748006X19890739
   Web of Science ®Google Scholar
• Zhou, R., Gebraeel, N., & Serban, N. (2012). Degradation modeling and monitoring of truncated degradation signals. IIE Transactions: Industrial Engineering Research & Development, 44(9), 793–803. https://doi.org/10.1080/0740817X.2011.618175
   Web of Science ®Google Scholar
• Zhou, S., Tang, Y., & Xu, A. (2021). A generalized Wiener process with dependent degradation rate and volatility and time-varying mean-to-variance ratio. Reliability Engineering and System Safety, 216, 107895. https://doi.org/10.1016/j.ress.2021.107895
   Web of Science ®Google Scholar