Improved canonical correlation analysis-based fault detection methods for industrial processes. (May 2016) - Chen, Zhiwen; Zhang, Kai; Ding, Steven X.; Shardt, Yuri A.W.; Hu, Zhikun
Abstract : Highlights: To investigate the methods for detecting incipient multiplicative faults. To propose methods combining the statistical local approach and CCA methods. To discuss the detection of incipient multiplicative faults using the T 2 statistic. To evaluate the new methods using a pilot scale CSTH and the Tennessee Eastman process. Abstract: Recent research has emphasized the successful application of canonical correlation analysis (CCA) to perform fault detection (FD) in both static and dynamic processes with additive faults. However, dealing with multiplicative faults has not been as successful. Thus, this paper considers the application of CCA to deal with the detection of incipient multiplicative faults in industrial processes. The new approaches incorporate the CCA-based FD with the statistical local approach. It is shown that the methods are effective in detecting incipient multiplicative faults. Experiments using a continuous stirred tank heater and simulations on the Tennessee Eastman process are provided to validate the proposed methods.