Distributed data-driven fault detection for industrial interconnected systems with unknown topology structure

Driven by the enhancing requirements for ensuring stable operation of complex industrial processes, this paper investigates a distributed data-driven fault detection (FD) method for industrial interconnected systems. The topology structure of the industrial interconnected system is presumed to be unknown. To this end, the input/output (I/O) data-set model for the interconnected system and the distributed input/output data-set models for the subsystems are derived first. Then, the topology structure of the interconnected system is reconstructed through data-driven realization of gap metric by utilizing the subspace identification method. Subsequently, the distributed data-driven fault detection method with enhanced information exchange topology is developed. Finally, we validate the feasibility and efficiency of the proposed method by conducting a case study on a chemical process consisting of four continuous-stirred tank reactors (CSTRs). Copyright (c) 2024 The Authors.