Step Stress Accelerated Degradation Process Modeling and Remaining Useful Life Estimation

2014 ◽  
Vol 50 (16) ◽  
pp. 33 ◽  
Author(s):  
Shengjin TANG
Keyword(s):  
Process Modeling ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Life Estimation ◽  
Accelerated Degradation ◽  
Useful Life

Stochastic degradation process modeling and remaining useful life estimation with flexible random-effects

2017 ◽  
Vol 354 (6) ◽  
pp. 2477-2499 ◽  
Author(s):  
Zhengxin Zhang ◽  
Changhua Hu ◽  
Xiaosheng Si ◽  
Jianxun Zhang ◽  
Jianfei Zheng
Keyword(s):  
Random Effects ◽  
Process Modeling ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Life Estimation ◽  
Useful Life

Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process

2012 ◽  
Vol 61 (1) ◽  
pp. 50-67 ◽  
Author(s):  
Xiao-Sheng Si ◽  
Wenbin Wang ◽  
Chang-Hua Hu ◽  
Dong-Hua Zhou ◽  
Michael G. Pecht
Keyword(s):  
Nonlinear Diffusion ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Life Estimation ◽  
Useful Life

scholarly journals Remaining Useful Life Estimation Considering Prior Accelerated Degradation Data and Bayesian Inference for Multi-Stress Operating Conditions

2020 ◽  
Vol 6 (1) ◽  
pp. 103-117
Author(s):  
Shah M. Limon ◽  
Om Prakash Yadav
Keyword(s):  
Asset Management ◽  
Field Performance ◽  
Degradation Process ◽  
Management Plan ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Degradation Behavior ◽  
Accelerated Degradation ◽  
Degradation Data ◽  
Useful Life

Prediction of remaining useful life using the field monitored performance data provides a more realistic estimate of life and helps develop a better asset management plan. The field performance can be monitored (indirectly) by observing the degradation of the quality characteristics of a product. This paper considers the gamma process to model the degradation behavior of the product characteristics. An integrated Bayesian approach is proposed to estimate the remaining useful life that considers accelerated degradation data to model degradation behavior first. The proposed approach also considers interaction effects in a multi-stress scenario impacting the degradation process. To reduces the computational complexity, posterior distributions are estimated using the MCMC simulation technique. The proposed method has been demonstrated with an LED case example and results show the superiority of Bayesian-based RUL estimation.

scholarly journals Nonlinear Step-Stress Accelerated Degradation Modeling and Remaining Useful Life Estimation Considering Multiple Sources of Variability

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 124558-124575 ◽  
Author(s):  
Zhenan Pang ◽  
Changhua Hu ◽  
Xiaosheng Si ◽  
Jianxun Zhang ◽  
Dangbo Du ◽  
...  
Keyword(s):  
Remaining Useful Life ◽  
Multiple Sources ◽  
Life Estimation ◽  
Accelerated Degradation ◽  
Degradation Modeling ◽  
Useful Life

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

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