Uncertainty-aware deep learning for reliable health monitoring in safety-critical energy systems

In recent years, significant advancements in deep learning technology have facilitated the development of intelligent health monitoring approaches for energy systems. However, when dealing with safety-critical energy systems, such as nuclear energy systems, conventional deep learning models with point estimation fail to account for the inherent uncertainty in the predictions. This limitation poses challenges for providing reliable and trustworthy decision support for critical operations. To overcome this challenge, this study proposes a novel intelligent monitoring approach that integrates uncertainty-aware deep neural networks. Firstly, a spatio-temporal state matrix-based signal preprocessing method is proposed to enhance feature extraction capabilities, enabling the effective integration of diverse multi-source data. Secondly, a probabilistic distribution is developed to generate predictive uncertainty for all network parameters, enabling the assessment of the confidence of the model's outputs not only for known operation scenarios but also for unknown scenarios. Finally, the experiments are conducted using an established advanced nuclear energy research platform and a public nuclear accident simulation platform, ensuring the effectiveness and applicability of the proposed approach in practical settings. Overall, the proposed approach significantly enhances the reliability and trustworthiness of the monitoring outputs while mitigating the risks associated with the decision-making process in safety-critical energy systems.