Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework

Fault diagnosis is efficient to improve the safety, reliability, and cost-effectiveness of industrial machinery. Deep learning has been extensively investigated in fault diagnosis, exhibiting state-of-the-art performance. However, since deep learning is inherently uninterpretable, the low trustworthiness of the diagnostic results given by these black-boxes has always been a limiting factor in industrial applications. Specially, the monitoring data under unforeseen domains will be easily misdiagnosed without any symptoms. To address this issue, this paper explores the fault diagnosis in a probabilistic Bayesian deep learning framework by exploiting an uncertainty aware model to understand the unknown fault information and identify the inputs from unseen domains, ultimately achieving trustworthy diagnosis. Moreover, the diagnostic uncertainty is decomposed in two aspects: (1) epistemic uncertainty, reflecting the discrepancy of test input relative to the training data, and (2) aleatoric uncertainty, referring to the noise originating from the input, offering a deep understanding of the unknowns in the diagnostic model. The proposed framework not only can accurately identify the faults belonging to a known distribution, but also provides insights into uncertainty and avoid the erroneous decision-making. Last, but not least, comprehensive diagnostic experiments considering unseen scenarios are used to demonstrate the effectiveness of proposed framework, providing competitive results.

Automated summary

This paper proposes a probabilistic Bayesian deep learning framework for fault diagnosis, which achieves trustworthy diagnosis by utilizing an uncertainty aware model. The framework accurately identifies faults and provides insights into uncertainty, avoiding erroneous decision-making, and demonstrates competitive results when considering unseen scenarios.