Experimentally Validated Adaptive Digital Twin for AI-Driven Fault Diagnosis and Predictive Health Monitoring of Multi-Machine Electric Drive Systems

This paper presents an adaptive digital twin framework for intelligent fault diagnosis and predictive health monitoring of a coupled multi-machine electric drive system. The experimental platform consists of two synchronous motors and a synchronous generator arranged in an interconnected electromechanical configuration and instrumented through the Lucas-Nülle laboratory platform. A physics-based digital representation is integrated with experimental measurements to reproduce the electrical and mechanical behavior of the drive system under different operating conditions. Physical-to-digital residuals are subsequently used for health assessment, fault detection, fault isolation, and intelligent classification. Experimental validation under five load conditions resulted in mean root-mean-square errors of 0.0391 N·m for torque, 0.1079 A for motor current, and 0.4555 V for motor voltage, with an overall mean normalized RMSE of 4.16%. The experimentally validated healthy-state digital twin was subsequently evaluated using model-based current-sensor, voltage-sensor, and torque-degradation scenarios. Channel-specific residual analysis successfully detected and isolated the abnormalities under investigation. Using digital twin residual features, an Ensemble classifier achieved a test accuracy of 98.61%, outperforming Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers. In addition, a prognostic indicator was developed to characterize progressive degradation, with threshold crossings observed at simulated severities of 1.26%, 12.44%, and 17.37% for voltage-sensor deviation, torque degradation, and current-sensor deviation, respectively. The results demonstrate that integrating experimentally validated digital twin modeling, residual-based diagnostics, intelligent classification, and degradation monitoring provides a unified framework for condition assessment of coupled multi-machine electric-drive systems.