A Reversible Residual Network-Aided Canonical Correlation Analysis to Fault Detection and Diagnosis in Electrical Drive Systems

To ensure the safety of electrical drive systems, fault detection and diagnosis (FDD) has become an active approach over the past two decades. Multivariate analysis is a popular method in FDD, among which canonical correlation analysis (CCA) has been widely applied and studied. However, most CCA-based fault detection (FD) methods assume that the signal is Gaussian and that there is a linear relationship between the variables. Since the electrical drive systems are nonlinear, these CCA-based FD methods are not optimal. With the help of the reversible residual network, this paper proposes a reversible residual network-aided CCA (RRNCCA) for fault diagnosis. The main work is as follows: 1) The objective function of RRNCCA is reformulated; 2) RRNCCA-based FDD is first designed for electrical drive systems; 3) Through the difference in FD results, fault diagnosis is directly achieved. The effectiveness of the proposed method is verified via an electrical drive system.