SIR-Aided Dynamic Canonical Correlation Analysis for Fault Detection and Isolation of Industrial Automation Systems

In this work, fault detection and isolation (FDI) of industrial automation systems with a closed-loop configuration is under consideration. Specifically, the mean of the input and output vectors is time-varying with the variation of the reference vectors. This brings a great challenge to the existing multivariate analysis-based methods, which are lack of consideration of closed-loop dynamics. To this end, a stable image representation (SIR)-aided dynamic canonical correlation analysis (SD-CCA)-based FDI method is proposed. In this method, residual generation is performed in two steps. Residual vectors of the closed-loop dynamic are first generated based on the identified data-driven SIR to remove the time-varying mean. Then, an SD-CCA-based residual generator is established, which enhances the fault detectability by considering the correlation between zero-mean input and output. Finally, by maximizing the fault direction angle, an optimal fault isolation method based on the fault direction angle of SD-CCA is proposed. It is followed by a sensitivity analysis of the proposed method, furthermore, whose performance is evaluated by comparing with several state-of-the-art methods on a numerical simulation and a real chiller system. Results show that the proposed method has a better FDI performance than the compared methods.