An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications. (January 2023) - Zhou, Taotao; Zhang, Laibin; Han, Te; Droguett, Enrique Lopez; Mosleh, Ali; Chan, Felix T.S.
Highlights: Propose an uncertainty-informed framework for trustworthy fault diagnosis. Build a probabilistic Bayesian CNN for uncertainty quantification in fault diagnosis. Discuss how to measure and use uncertainty to guide decision-makers. Validate the framework by bearing fault diagnosis with irrelevant data, sensor faults and unknown faults. Abstract: 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 trustworthy 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.