Conditional Invertible Neural Network-Based Fault Detection and Diagnosis in Permanent Magnet Synchronous Motor Drive System

This article presents a data-driven framework for fault detection and diagnosis (FDD) in permanent magnet synchronous motor (PMSM) drive systems using conditional invertible neural network (CINN). Unlike conventional deep models that operate as closed boxes, the proposed CINN framework establishes a bidirectional and control-theoretically consistent mapping between process data and residual signals, enabling explainable fault reasoning. First, an analytically interpretable formulation of the CINN-based residual mapping is derived using composite operators, and two sets of residual signals are designed to enhance FDD. Second, a Bayesian classification-based fault diagnosis strategy is implemented through a detailed analysis of the residual signals. The proposed FDD scheme offers the following advantages: 1) the system modeling and residual construction are integrated into the CINN; and 2) its design is grounded in control-theoretic principles to ensure the theoretical soundness of the learning mechanism. Experimental results verify that the proposed method achieves fast and accurate fault detection along with effective diagnosis for various fault types in PMSM drive systems.