  There has been a growing interest in deep learning-based prognostic and
health management (PHM) for building end-to-end maintenance decision support
systems, especially due to the rapid development of autonomous systems.
However, the low trustworthiness of PHM hinders its applications in
safety-critical assets when handling data from an unknown distribution that
differs from the training dataset, referred to as the out-of-distribution (OOD)
dataset. To bridge this gap, we propose an uncertainty-informed framework to
diagnose faults and meanwhile detect the OOD dataset, enabling the capability
of learning unknowns and achieving trustworthy fault diagnosis. Particularly,
we develop a probabilistic Bayesian convolutional neural network (CNN) to
quantify both epistemic and aleatory uncertainties in fault diagnosis. The
fault diagnosis model flags the OOD dataset with large predictive uncertainty
for expert intervention and is confident in providing predictions for the data
within tolerable uncertainty. This results in trustworthy fault diagnosis and
reduces the risk of erroneous decision-making, thus potentially avoiding
undesirable consequences. The proposed framework is demonstrated by the fault
diagnosis of bearings with three OOD datasets attributed to random number
generation, an unknown fault mode, and four common sensor faults, respectively.
The results show that the proposed framework is of particular advantage in
tackling unknowns and enhancing the trustworthiness of fault diagnosis in
safety-critical applications.
