A combined canonical variate analysis and Fisher discriminant analysis (CVA-FDA) approach for fault diagnosis

摘要

This paper proposes a combined canonical variate analysis (CVA) and Fisher discriminant analysis (FDA) scheme (denoted as CVA-FDA) for fault diagnosis, which employs CVA for pretreating the data and subsequently utilizes FDA for fault classification. In addition to the improved handling of serial correlations in the data, the utilization of CVA in the first step provides similar or reduced dimensionality of the pretreated datasets compared with the original datasets, as well as decreased degree of overlap. The effectiveness of the proposed approach is demonstrated on the Tennessee Eastman process. The simulation results demonstrate that (ⅰ) CVA-FDA provides better and more consistent fault diagnosis than FDA, especially for data rich in dynamic behavior; and (ⅱ) CVA-FDA outperforms dynamic FDA in both discriminatory power and computational time.