RESEARCH PAPER
Artificial neural network supported monotonic stochastic processes for reliability analysis considering multi-uncertainties
1
Beihang University, China
Submission date: 2024-08-31
Final revision date: 2024-09-25
Acceptance date: 2024-12-08
Online publication date: 2024-12-14
Publication date: 2024-12-14
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(3):197051
HIGHLIGHTS
- Process, mean function and parameter uncertainties are considered simultaneously.
- IG and Gamma processes are cooperated with ANN.
- BMA method is introduced to estimate the model parameters and probabilities.
KEYWORDS
reliability analysisArtificial neural networkStochastic processMonotonic degradationMulti-uncertainties
TOPICS
ABSTRACT
Focusing on nonmonotonic degradation processes, we have cooperated artificial neural network (ANN) with Wiener process to utilize its powerful ability on curve fitting. While, the degradation processes of some actual products are determined as monotonic. Furthermore, the process uncertainty issue is also neglected, which is inevitable in engineering practice. Hence, focusing on monotonic degradation dataset, this research introduces ANN-based stochastic process for reliability analysis under multiple uncertainties, including random effects, process uncertainty and mean function uncertainty. The ANN-supported inverse Gaussian and Gamma process models subject to random effects are built. The related parameter estimation and updating methods are also constructed by utilizing moment estimation (ME), Akaike information criterion (AIC) and fully Bayesian inference methods. According to the simulation experiment and actual case study, the proposed method provides higher accuracies on population degradation modeling and monitoring individual degradation prediction.
ACKNOWLEDGEMENTS
This study was co-supported by the National Natural Science Foundation of China (52105045, 52475046), the Aviation Science Foundation (2022Z027051001), Ningbo Key R&D Program (2023Z010), Beijing Natural Science Foundation (L221008) and the Foundation of Tianmushan Laboratory (TK-2023-C-006).
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