Data-Driven Optimal Distributed Fault Detection Based on Subspace Identification for Large-Scale Interconnected Systems

Abstract

This article investigates the problem of data-driven distributed optimal fault detection for large-scale interconnected systems with the unmeasurable interaction term of neighboring system information. For large-scale systems, the computational and storage burdens hinder the application of centralized fault detection methods, while the existence of the unknown interaction term in residual generators brings challenges to distributed fault detection problems. To solve the above problems, the unknown interaction term is implicitly included in each subsystem through an algebraic equivalent transformation, so that the residual generator constructed by the distributed method will not lose the fault information propagated along the network topology. Furthermore, an optimization scheme is designed to measure the effect of the residual signal on noise and faults in all dimensions of the parity space, making the residual generator sufficiently sensitive to even weak faults. Numerical examples and a real hot strip rolling case verify the effectiveness and superiority of the proposed method.