Closed-Loop Identification of the Data-Driven SKR with Deterministic Disturbance for Fault Detection

Abstract

Industrial systems are always subjected to the deterministic disturbance due to some inherited factors, which is likely to degrade the control and monitoring performance to some extent. This paper presents an approach to the closed-loop subspace identification of the data-driven stable kernel representation (SKR) with the deterministic disturbance. The essence is that we extend the CSIMPCA algorithm by introducing the deterministic disturbance and subsequently separate the part corresponding to the SKR of the system from the obtained parity space. The inspiration for the idea mainly stems from the necessity for the identification and process monitoring of practical closed-loop systems. The effectiveness of the proposed method is demonstrated and illustrated through randomly generated 4-order MIMO discrete-time LTI systems. Furthermore, the identified SKR is finally applied to the fault detection and related experimental results show a decent detection performance.