Sensor fault identification based on time-lagged PCA in dynamic processes

Abstract Principal component analysis (PCA) is widely employed as a multivariate statistical method for fault detection, isolation and diagnosis in chemical processes. Previously, PCA has been successfully used to identify faulty sensors under normal static operating conditions. In this paper, we extend the reconstruction-based sensor fault isolation method proposed by Dunia et al. to dynamic processes. We develop a new method for identifying and isolating sensor faults in an inherent dynamic system. First, we describe how to reconstruct noisy or faulty measurements in dynamic processes. The reconstructed measurements are obtained by simple iterative optimization based on the correlation structure of the time-lagged data set. Then, based on the sensor validity index (SVI) approach developed by Dunia et al., we propose an SVI for fault isolation in dynamic processes. The proposed method was applied to sensor fault isolation in two strongly dynamic systems: a simulated 4×4 dynamic process and a simulated wastewater treatment process (WWTP). In these experiments, the proposed sensor fault identification method correctly and rapidly identified the faulty sensor; in contrast, the traditional PCA-based sensor fault isolation approach showed unsatisfactory results when applied to the same systems.