A generic physics-informed neural network-based framework for reliability assessment of multi-state systems. (January 2023)

• Record Type: Journal Article
• Authors: Zhou, Taotao; Zhang, Xiaoge; Droguett, Enrique Lopez; Mosleh, Ali
• Is Part Of: Reliability engineering & system safety. Volume 229(2023)
• Publication Date: 2023-01
• Subjects: PINN Physics-Informed Neural Network -- MSS Multi-State System -- MCS Monte Carlo Simulation -- PCGrad Projecting Conflicting Gradients -- CTMC continuous-time Markov chain; Multi-state systems -- Reliability assessment -- Physics-informed neural network -- Gradient projection -- Markov process; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108835
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: In this paper, we develop a generic physics-informed neural network (PINN)-based framework to assess the reliability of multi-state systems (MSSs). The proposed framework follows a two-step procedure. In the first step, we recast the reliability assessment of MSS as a machine learning problem using the framework of PINN. A feedforward neural network with two individual loss groups is constructed to encode the initial condition and the state transitions governed by ordinary differential equations in MSS, respectively. Next, we tackle the problem of high imbalance in the magnitudes of back-propagated gradients from a multi-task learning perspective and establish a continuous latent function for system reliability assessment. Particularly, we regard each element of the loss function as an individual learning task and project a task's gradient onto the norm plane of any other task with a conflicting gradient by taking the projecting conflicting gradients (PCGrad) method. We demonstrate the applications of the proposed framework for MSS reliability assessment in a variety of scenarios, including time-independent or dependent state transitions, where system scales increase from small to medium. The computational results indicate that PINN-based framework reveals a promising performance in MSS reliability assessment and incorporation of PCGrad into PINN substantially improves the solution quality and convergence speed of the algorithm. Highlights: Provide a novel perspective on MSS reliability assessment from deep learning. Develop a generic physics-informed neural network (PINN)-based framework for MSS reliability assessment. Mitigate instability of training PINN from a multi-task learning perspective. Compare PINN's solutions with several state-of-the-art methods in different contexts.