An artificial neural network supported Wiener process based reliability estimation method considering individual difference and measurement error

Di Liu, Shaoping Wang and Xiaoyu Cui

Reliability Engineering and System Safety, 2022, vol. 218, issue PB

Abstract: Due to the powerful ability of artificial neural network in data fitting, it has been applied to describe the mean function in Wiener process for degradation modeling and estimating reliability. However, the previously published method neglects the measurement error and individual difference, which need to be considered in reliability estimation. In order to handle the above issues, the artificial neural network supported Wiener process is improved. Both the individual difference and measurement error are considered. The measurement error is described by a normal distribution with zero mean. The individual difference is described by introducing hyper parameters. In order to demonstrate the proposed method, a simulation study and a case study have been performed. It can be seen that, considering the measurement error and individual difference can improve the reliability and lifetime estimation accuracies, resulting in the proposed method is more powerful and more practical in engineering practice.

Keywords: Reliability estimation; Artificial neural network; Wiener process; Measurement error; Individual difference (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:218:y:2022:i:pb:s0951832021006499

DOI: 10.1016/j.ress.2021.108162

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