An artificial neural network supported stochastic process for degradation modeling and prediction

•Stochastic process is combined with ANN to handle degradation path uncertainty.•The hyper parameters are evaluated by moment estimation offline.•The process parameters are updated by Bayesian inference for online prediction.•Without path information the ANN supported stochastic process is still pra...

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Vydáno v:Reliability engineering & system safety Ročník 214; s. 107738
Hlavní autoři: Liu, Di, Wang, Shaoping
Médium: Journal Article
Jazyk:angličtina
Vydáno: Barking Elsevier Ltd 01.10.2021
Elsevier BV
Témata:
Artificial neural network
Artificial neural networks
Bayesian analysis
Degradation
Degradation modeling
Degradation path uncertainty
Degradation prediction
Information processing
Mathematical models
Modelling
Neural networks
Predictions
Process parameters
Reliability engineering
Statistical inference
Stochastic models
Stochastic process
Stochastic processes
Artificial neural network
Stochastic process
Degradation path uncertainty
Degradation prediction
Degradation modeling
ISSN:0951-8320, 1879-0836
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Shrnutí:•Stochastic process is combined with ANN to handle degradation path uncertainty.•The hyper parameters are evaluated by moment estimation offline.•The process parameters are updated by Bayesian inference for online prediction.•Without path information the ANN supported stochastic process is still practical.•The assumption that the initial degradation is zero is also freed. An artificial neural network supported stochastic process for degradation modeling and prediction is proposed in this paper. An artificial neural network is applied to describe the degradation path due to data fitting flexibility and path description considering degradation path uncertainty. The assumption that the initial degradation is zero in the stochastic process is freed. The artificial neural network supported stochastic process is trained by minimizing the minus log-likelihood offline-based on the run-to-failure degradation data. Considering unit-to-unit variance in population degradation modeling, the process parameters are assumed to be randomly distributed. Here, three common distributions describe the process parameters, and Akaike information criteria applied to select distributions of process parameters. The process parameters are evaluated by Bayesian inference based on the trained path, and distributions of process parameters are based on real-time degradation data for online prediction. The proposed method is verified by a simulation experiment based on a Wiener process, which seemed a true model. Furthermore, an actual degradation dataset is also applied to illustrate the effectiveness of the proposed method. Both the simulation experiment and actual example indicate that the proposed stochastic process is capable of degradation modeling and degradation predicting, even without prior information about the degradation path.
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ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2021.107738