Performance Analysis of Dynamic PCA for Closed-Loop Process Monitoring and Its Improvement by Output Oversampling Scheme

Abstract

The performance of dynamic principal component analysis (DPCA)-based fault detection and diagnosis in a closed-loop system is studied and its improvement by the output oversampling scheme is proposed in this paper. By the subspace decomposition technique, DPCA with the closed-loop data for fault detection does not perform better than DPCA with the open-loop data. Moreover, using fault reconstruction based on DPCA to determine the root cause would also become invalid in the closed loop. To eliminate the adverse effect of feedback control on the performance of the DPCA model, a new algorithm that directly constructs DPCA based on the closed-loop data is investigated using the output oversampled data without excitations in the reference signals. The associated enhanced characteristics of the sampled data in the output oversampling scheme are analyzed. A simulated continuous stirred tank heater illustrates that the proposed algorithm can significantly improve the DPCA performance of process monitoring and fault reconstruction in closed-loop systems.