Modelling omnipresent AI embedding cyber-physical systems by using a novel invariant-based, quantum-inspired fault detection and Bayesian diagnosis approach

Abstract

This study developed an omnipresent AI-driven online fault detection and diagnostic model for cyber-physical systems (CPS) within an enterprise information system. This model uses an invariant-based, quantum-inspired approach to detect faults in CPSs. This approach considerably reduces the computational overhead in fault detection, thus enhancing the feasibility of real-time CPS monitoring in industrial applications. Moreover, the developed model employs a Bayesian inference method for fault diagnosis. This method makes it possible to identify fault causes even when limited data are available. The developed model can substantially improve the fault detection efficiency and diagnostic reasoning of a CPS.