The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
Stochastic modelling and analysis of degradation for highly reliable products
Abstract
Degradation models have become an important analytic tool for complex systems. During the last two decades, a number of degradation models have been developed to capture the degradation dynamics of a system and aid the subsequent decision‐makings. This paper is aimed at providing a summary of the state of the arts in the field, and discussing some further research issues from both analytical and practical point of view. In this paper, degradation models are classified into three classes, that is, stochastic process models, general path models, and other models beyond these two classes. A review on the three classes is given with emphasis on the class of stochastic process models. A comprehensive comparison between stochastic process models and general path models is given to expound the pros and cons of these two methods. Applications of degradation models in degradation test planning and burn‐in modelling will also be discussed. Copyright © 2014 John Wiley & Sons, Ltd.
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Citations
Cites
A review on machinery diagnostics and prognostics implementing condition-based maintenance
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2005.09.012 · Cited by 4,816
Statistical Methods for Reliability Data
Nelson, W. (1998) · Technometrics · doi:10.1080/00401706.1998.10485526 · Cited by 2,504
Remaining useful life estimation – A review on the statistical data driven approaches
Si, X.-S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011) · European Journal of Operational Research · doi:10.1016/j.ejor.2010.11.018 · Cited by 2,162
A survey of the application of gamma processes in maintenance
van Noortwijk, J. M. (2009) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2007.03.019 · Cited by 1,359
Statistical Methods for Reliability Data
Nelson, W., Meeker, W. Q., & Escobar, L. A. (1998) · Technometrics · doi:10.2307/1271181 · Cited by 1,214
Using Degradation Measures to Estimate a Time-to-Failure Distribution
Lu, C. J., & Meeker, W. O. (1993) · Technometrics · doi:10.1080/00401706.1993.10485038 · Cited by 1,161
He, W., Williard, N., Osterman, M., & Pecht, M. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.08.040 · Cited by 1,105
Prognostics methods for battery health monitoring using a bayesian framework
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2005965 · Cited by 831
An ensemble model for predicting the remaining useful performance of lithium-ion batteries
Xing, Y., Ma, E. W. M., Tsui, K.-L., & Pecht, M. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2012.12.003 · Cited by 673
Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process
Si, X.-S., Wang, W., Hu, C.-H., Zhou, D.-H., & Pecht, M. G. (2012) · IEEE Transactions on Reliability · doi:10.1109/tr.2011.2182221 · Cited by 662
Cited by
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.016 · Cited by 2,634
Zhang, Z., Si, X., Hu, C., & Lei, Y. (2018) · European Journal of Operational Research · doi:10.1016/j.ejor.2018.02.033 · Cited by 696
Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators
Wang, D., Tsui, K.-L., & Miao, Q. (2018) · IEEE Access · doi:10.1109/access.2017.2774261 · Cited by 455
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies
Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B. D., Todd, M. D., Mahadevan, S., Hu, C., & Hu, Z. (2022) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-022-03425-4 · Cited by 441
RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model
Zhai, Q., & Ye, Z.-S. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2684821 · Cited by 363
Self-citation
Metadata
- Authors
- Ye, Z., & Xie, M.
- Venue
- Applied Stochastic Models in Business and Industry
- Publisher
- Wiley
- Published
- 2014-10-10
- Volume
- 31
- Issue
- 1
- Pages
- 16-32
- ISSN
- 1524-1904 (ISSN-L)
- 1526-4025
- OpenAlex
- W1932372884
- Cited by
- 685
- References
- 74
- FWCI
- 94.21
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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