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

• A new fault diagnosis algorithm for serially correlated data is proposed.
• Canonical variate analysis (CVA) is coupled to Fisher discriminant analysis (FDA).
• The proposed approach is demonstrated on the Tennessee Eastman process.
• Simulation results demonstrate improved fault diagnosis for serially correlated data.
• The method outperforms dynamic FDA in discriminatory power and computational time.

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.