Digital Twin-Based Real-Time Monitoring and Predictive Fault Diagnosis of Complex Electrical Circuit Systems in Industrial

Abstract

Modern industrial automation relies heavily on complex electrical circuit systems, including variable frequency drives (VFDs), power electronic converters, switchgear, and multi-phase motor control units. Unexpected electrical faults, insulation degradation, component wear, and harmonic distortion lead to unplanned downtime, safety hazards, and catastrophic capital loss. Traditional condition monitoring strategies rely on threshold based alarm systems or offline periodic testing, which fail to capture high frequency transient dynamics or predict incipient degradation pathways. This paper presents a complete review and technical framework for Digital Twin Based Real-Time Monitoring and Predictive Fault Diagnosis in complex electrical systems. A high-fidelity Digital Twin architecture is constructed by coupling multi-physics finite element models and dynamic circuit state-space equations with high-bandwidth IoT sensor telemetry. Machine learning algorithms, specifically Convolutional Neural Networks (CNNs) integrated with Long Short-Term Memory (LSTM) units, process continuous telemetry streams (voltage, current, temperature, thermal imaging, and high-frequency acoustic emission) to detect micro-transient anomalies. Quantitative evaluation across simulated and experimental industrial electrical drives demonstrates a fault identification accuracy of 98.6%, with an average lead time for predictive insulation degradation detection of 72 hours prior to functional failure. The integrated framework reduces false-alarm rates by 64.2% compared to conventional threshold monitoring and elevates Overall Equipment Effectiveness (OEE) from 71.4% to 88.9%. This study delivers actionable architecture guidelines for implementing edge-cloud hybrid digital twins in smart industrial power infrastructure.

KEYWORDS: Digital Twin, Real-Time Condition Monitoring, Predictive Fault Diagnosis, Complex Electrical Circuits, Industrial Automation, Power Electronics, Deep Learning (CNN-LSTM).