Zhiwen Chen

TL;DR: An FD technique combining the generalized CCA with the threshold-setting based on the randomized algorithm is proposed and applied to the simulated traction drive control system of high-speed trains and shows that the proposed method is able to improve the detection performance significantly in comparison with the standard generalized C CA-based FD method.

TL;DR: The key performance indicator-based multivariate statistical process monitoring and fault diagnosis methods for linear static processes are surveyed and evaluated using the multivariate statistics framework and are broadly classified into three categories: direct, linear regression-based, and PLS-based.

TL;DR: In this paper, canonical correlation analysis (CCA)-based fault detection methods are proposed for both static and dynamic processes, which are applied to an alumina evaporation process, and the achieved results show that both methods are applicable for fault detection.

TL;DR: A new data-driven fault detection method based on distributed canonical correlation analysis (D-CCA) is proposed to address the plant-wide process monitoring problem and the false alarm rate, fault detection rate, and the detection delay are comparable, suggesting that the proposed method is feasible.

TL;DR: In this paper, the authors considered the application of canonical correlation analysis (CCA) to deal with the detection of incipient multiplicative faults in industrial processes and incorporated the CCA-based FD with the statistical local approach.