Physics-informed deep learning: A promising technique for system reliability assessment

Abstract

Deep learning-based models for system prognostics and health management have received significant attention in the reliability and safety fields. However, limited progress has been achieved in the usage of deep learning for system reliability assessment. This paper aims to bridge this gap and explore the interface between deep learning and system reliability assessment by expanding and adapting recent advances in physics-informed deep neural networks. Particularly, we present a novel deep learning-based system reliability assessment and develop a physics-informed generative adversarial network-based approach to facilitate uncertainty quantification and propagation as well as enable measurement data fusion and incorporation into system reliability assessment. Three numerical examples employing a dual-processor computer system are used to demonstrate the proposed approach. Results show that the proposed approach has comparable performance to the widely used Runge–Kutta method and Monte Carlo simulation in handling deterministic scenarios. When dealing with probabilistic scenarios, the proposed approach is 16.5 times more computationally efficient than Monte Carlo simulation in uncertainty quantification and is effective in fusing measurement data for the system’s reliability assessment. The proposed approach offers a novel perspective and builds a link between deep learning and system reliability assessment for computational alleviation and data assimilation challenges.