Hybrid digital twin-based fault diagnosis framework for PMSMs in electric vehicle applications

Abstract

The increasing demand for reliable and efficient operation of Permanent Magnet Synchronous Motors (PMSMs) in electric vehicle (EV) motor systems necessitates advanced real-time fault detection techniques. This study presents a Hybrid Digital Twin (HDT) framework that integrates Physics-Informed Neural Networks (PINNs) and an Extended Kalman Filter (EKF)-based sensor fusion strategy to enable accurate, adaptive, and real-time condition monitoring and fault diagnosis of PMSMs. Initially, motor signals are transformed into the rotating reference frame using the dq-axis transformation to facilitate simplified modeling. The PINN model, informed by fundamental motor physics, estimates stator flux linkages from voltage and current inputs. These estimates are refined by the EKF, which mitigates sensor noise and model uncertainties, enhancing the fidelity of state estimation. Residuals between measured and predicted signals are analyzed, and a Support Vector Machine (SVM) classifier is used to detect and categorize faults. ►The proposed HDT framework achieves a fault detection accuracy of 96.99%, outperforming traditional Motor Current Signature Analysis (MCSA) by approximately 13%. The model also achieves high precision (90.09%), recall (89.21%), and F1-score (89.65%), with a fault detection latency of under 95 milliseconds and a 31% reduction in false positives. The combined use of physics-based flux estimation, real-time EKF correction, residual analysis, and FFT-based spectral features ensures reliable and interpretable diagnostics. Overall, the HDT framework offers a scalable and robust solution for real-time PMSM fault detection in EV motor applications, particularly under dynamic load conditions. • Hybrid digital twin detects PMSM faults in EVs with 96.99% accuracy. • Real-time fault diagnosis achieved in 95 ms with 33% fewer false alarms. • PINNs reduce need for large labeled data, enabling scalable models. • RL-based threshold tuning boosts accuracy under dynamic conditions. • dq-transform, FFT, and EKF fusion ensure robust time-frequency analysis.

Topics & Concepts