Fault Detection and Monitoring in Induction Machines Using Data-Driven Model Drift Detection

Stator short-circuit faults (SSCFs) account for a significant portion of induction motor failures, yet their early detection remains a challenge in industrial environments where labeled fault data is scarce and installing additional sensors is often impractical. This paper proposes a novel, data-driven fault detection and diagnosis framework grounded in the Residual Information Value (RIV) principle to overcome reliability limitations of traditional spectral and residual energy methods. By redefining fault detection as a statistical test of independence between control inputs (voltages) and current residuals, the proposed method identifies incipient faults as model drifts without relying on prior knowledge of fault distributions. A key contribution of this work is the seamless integration of the diagnostic scheme into standard Variable Speed Drives (VSDs): the healthy nominal model (a Multilayer Perceptron) is trained exclusively using data from the drive's existing self-commissioning routine, eliminating the need for manual data collection or complex physical parameter identification. Experimental validation on an industrial test bench demonstrates that the framework achieves superior diagnostic performance compared to traditional baselines, providing higher statistical separability and a reduced false alarm rate. The system can detect 1% incipient faults in approximately 61 ms while accurately identifying the faulty phase. The results confirm that the proposed RIV-based strategy offers a robust, non-intrusive, and industry-ready solution for predictive maintenance that effectively balances high-speed detection with enhanced statistical reliability.