An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications

Deep learning-based models, while highly effective for prognostics and health management, fail to reliably detect the data unknown in the training stage, referred to as out-of-distribution (OOD) data. This restricts their use in safety-critical assets, where unknowns may impose significant risks and cause serious consequences. To address this issue, we propose to leverage predictive uncertainty as a sign of trustworthiness that aids decision-makers in comprehending fault diagnostic results. A novel probabilistic Bayesian convolutional neural network (PBCNN) is presented to quantify predictive uncertainty instead of deterministic deep learning, so as to develop a trust-worthy fault diagnosis framework. Then, a predictive risk-aware strategy is proposed to guide the fault diagnosis model to make predictions within tolerable risk limits and otherwise to request the assistance of human experts. The proposed method is capable of not only achieving accurate results, but also improving the trustworthiness of deep learning-based fault diagnosis in safety-critical applications. The proposed framework is demonstrated by fault diagnosis of bearings using three types of OOD data. The results show that the proposed framework has high accuracy in handling a mix of irrelevant data, and also maintains good performance when dealing with a mix of sensor faults and unknown faults, respectively.

Automated summary

This paper proposes a reliable fault diagnosis method based on deep learning, which improves the reliability and accuracy of deep learning-based fault diagnosis in safety-critical applications by quantifying predictive uncertainty and introducing a predictive risk-aware strategy.