Digital Twin-Based Online Diagnosis Method for Inter-Turn Short Circuit Fault in Stator Windings of Induction Motors

Inter-turn short-circuit fault is a common electrical issue in high-speed train traction motors, which can severely degrade motor performance and significantly shorten operational lifespan. Early detection is crucial for ensuring the safety of traction systems. This paper presents a digital twin-based method for diagnosing stator winding inter-turn short-circuit faults in induction motors. First, an advanced rapid-solving algorithm is employed to establish a real-time digital twin model of the motor under healthy conditions. Second, a mathematical model characterizing stator winding faults is developed. Subsequently, fault detection and localization are achieved through analyzing three-phase current residuals between the digital twin model and the actual system. Extensive simulations and experiments demonstrate that the proposed method generates a fault index amplitude approximately 20 times larger than traditional sampling-value-based prediction methods, indicating exceptional sensitivity. The approach is minimally invasive, requiring no additional measurement equipment. Moreover, it maintains diagnostic capability even under motor parameter mismatch conditions, outperforming traditional methods. The proposed method demonstrates distinct advantages for high-speed train traction systems. It enables real-time monitoring and predictive maintenance, effectively reducing operational costs while preventing catastrophic failures.