Trustworthy Bayesian deep learning framework for uncertainty quantification and confidence calibration: Application in machinery fault diagnosis
Hao Li,
Jinyang Jiao,
Zongyang Liu,
Jing Lin,
Tian Zhang and
Hanyang Liu
Reliability Engineering and System Safety, 2025, vol. 255, issue C
Abstract:
Reliable and accurate machinery fault diagnosis is crucial for ensuring operational safety and reducing downtime in industrial settings. Traditional intelligent diagnosis methods only focus on improving the accuracy of in-distribution samples, but neglect the trustworthiness evaluation of diagnosis results. To address these issues, this paper developed a novel trustworthy machinery fault diagnosis (TMFD) method, which integrates Bayesian deep learning techniques with model calibration strategies. Specifically, TMFD regards a Bayesian convolutional neural network framework as the backbone. Then, we introduce α-divergence to facilitate the decomposition and quantification of epistemic uncertainty and aleatoric uncertainty, ultimately achieving out-of-distribution sample detection through epistemic uncertainty. Then, the ante-calibration loss constraint and the compositional post-calibration operation are jointly applied to promote data-efficient and high expressive calibration for in-distribution sample diagnosis confidence. Finally, TMFD is validated using three experimental datasets, demonstrating its effectiveness and robustness in machinery fault diagnosis.
Keywords: Trustworthy; Bayesian deep learning; Uncertainty quantification; Model calibration; Machinery fault diagnosis (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:255:y:2025:i:c:s0951832024007282
DOI: 10.1016/j.ress.2024.110657
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