Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis

Abstract

Principal component analysis (PCA) is a well-known data dimensionality technique that has been used to detect faults during the operation of industrial processes. Dynamic principal component analysis (DPCA) and canonical variate analysis (CVA) are data dimensionality techniques which take into account serial correlations, but their effectiveness in detecting faults in industrial processes has not been extensively tested. In this paper, score/state and residual space PCA, DPCA, and CVA are applied to the Tennessee Eastman process simulator, which was designed to simulate a wide variety of faults occurring in a chemical plant based on a facility at Eastman Chemical. This appears to be the first application of residual space CVA statistics for detecting faults in a large-scale process. Statistics quantifying variations in the residual space were usually more sensitive but less robust to the faults than the statistics quantifying the variations in the score or state space. The statistics exhibiting a small missed detection rate tended to exhibit small detection delays and vice versa. A residual-based CVA statistic proposed in this paper gave the best overall sensitivity and promptness, but the initially proposed threshold for the statistic lacked robustness. This motivated increasing the threshold to achieve a specified missed detection rate.