Fault Detection Based on Canonical Correlation Analysis with Rank Constrained Optimization

Canonical correlation analysis (CCA) has attracted increasing attention in the field of fault detection because it provides an effective way to explore the relationship between the input and output data. This paper develops a novel rank constrained CCA (RCCCA) framework, which is the first approach that takes the rank prior information into consideration. Technically, the rank constrained optimization is able to capture the global structures of variables, and thus improve the performance of fault detection. In order to solve RCCCA, an alternating minimization algorithm is designed, which aims to preserve the maximum correlation with the low-rank learning. A fault detection residual is then generated, and the test statistic is constructed to determine whether a fault occurs. The RCCCA-based fault detection is finally tested on a numerical example and the Tennessee Eastman benchmark process. Monitoring results indicate the efficiency and feasibility of the proposed method.