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Volume 11 Issue 6
Jun.  2024

IEEE/CAA Journal of Automatica Sinica

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J. Ren, J. Wen, Z. Zhao, R. Yan, X. Chen, and  A. Nandi,  “Uncertainty-aware deep learning: A promising tool for trustworthy fault diagnosis,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1317–1330, Jun. 2024. doi: 10.1109/JAS.2024.124290
Citation: J. Ren, J. Wen, Z. Zhao, R. Yan, X. Chen, and  A. Nandi,  “Uncertainty-aware deep learning: A promising tool for trustworthy fault diagnosis,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1317–1330, Jun. 2024. doi: 10.1109/JAS.2024.124290

Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

doi: 10.1109/JAS.2024.124290
Funds:  This work was supported in part by the National Natural Science Foundation of China (52105116), Science Center for gas turbine project (P2022-DC-I-003-001), and the Royal Society award (IEC\NSFC\223294) to Professor Asoke K. Nandi
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  • Recently, intelligent fault diagnosis based on deep learning has been extensively investigated, exhibiting state-of-the-art performance. However, the deep learning model is often not truly trusted by users due to the lack of interpretability of “black box”, which limits its deployment in safety-critical applications. A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases, and the human in the decision-making loop can be found to deal with the abnormal situation when the models fail. In this paper, we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks, called SAEU. In SAEU, Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural networks. Based on the SAEU, we propose a unified uncertainty-aware deep learning framework (UU-DLF) to realize the grand vision of trustworthy fault diagnosis. Moreover, our UU-DLF effectively embodies the idea of “humans in the loop”, which not only allows for manual intervention in abnormal situations of diagnostic models, but also makes corresponding improvements on existing models based on traceability analysis. Finally, two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.


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    • The Multivariate Gaussian distribution is employed into the deep architecture and some methods of representing diversity are combined to quantify both aleatoric and epistemic uncertainties simultaneously, which can effectively reduce the computational complexity of quantifying aleatoric and epistemic uncertainties in intelligent fault diagnosis
    • On the basis of uncertainty decomposition graph given by our simplified algorithm, we proposed a unified trustworthy fault diagnosis framework, named as UU-DLF. It gives the “black box” models a certain degree of post-hoc interpretability, which helps to realize model failure warning and model improvement from the perspective of uncertainty decomposition
    • Through the powerful performance and promising prospects shown by experiments, the uncertainty of deep learning proposed by our paper may provide a promising way for deep learning models to gain industrial users’ trust


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