Digital Twin-Based Condition Monitoring and Fault Diagnosis of Industrial AC Motor Drive Systems: A Comprehensive Review

Abstract

ABSTRACT Industrial AC motor drive systems serve as the foundational backbone of modern automated manufacturing, heavy material handling, and processing plants. Sudden operational disruptions or catastrophic failures in these electromechanical drive trains lead to extensive unplanned downtime, substantial monetary losses, and critical safety hazards. Traditional condition monitoring and fault diagnosis methods, such as threshold-based signal analysis, Motor Current Signature Analysis (MCSA), and standalone deep learning classifiers, face severe limitations when applied to dynamic speed load profiles, non-stationary operation, and early-stage compound fault detection. Recently, Digital Twin (DT) technology has emerged as a transformative paradigm that bridges physical assets with high-fidelity virtual models by integrating real-time telemetry, multi-physics analytical formulations, and adaptive data-driven machine learning algorithms. This review paper provides a rigorous technical examination of Digital Twin-based condition monitoring and fault diagnosis for industrial AC motor drive systems. We systematically examine the architectural taxonomy of DT platforms, analyzing physical-to-virtual synchronized mapping, physics informed neural networks (PINNs), hybrid modeling strategies, and remaining useful life (RUL) prognostic engines. Furthermore, the paper evaluates fault diagnostic capabilities across stator inter-turn short circuits, bearing degraded states, rotor bar fractures, and inverter switching anomaly modes. Through quantitative performance benchmarks and detailed comparative analysis, we demonstrate that DT-driven hybrid architectures achieve fault diagnostic accuracies exceeding 98.6% under highly variable operational conditions while lowering false alarm rates to below 0.5%. Critical research gaps, edge-cloud compute constraints, domain adaptation hurdles, and cyber physical security vulnerabilities are thoroughly dissected, culminating in strategic future research directions toward fully autonomous, self-optimizing electromechanical drive systems.

KEYWORDS: Digital Twin, AC Motor Drives, Condition Monitoring, Fault Diagnosis, Physics-Informed Neural Networks, Remaining Useful Life (RUL), Predictive Maintenance.