Development of an Artificial Intelligence-Based Monitoring System For Early Detection of Technical Failures of Electrical Equipment

Annotatsiya

The increasing complexity of modern electrical infrastructures demands advanced monitoring solutions capable of identifying technical failures at their earliest stages. Traditional diagnostic approaches, based on periodic inspections and fixed threshold analysis, are often unable to detect subtle degradation patterns that lead to unexpected equipment failures and costly downtime. This article proposes an artificial intelligence-based monitoring system designed to enhance early fault detection in electrical equipment such as transformers, electric motors, switchgear, and high-voltage components. The system integrates real-time sensor data acquisition, machine learning algorithms, anomaly detection models, and digital twin technology to provide continuous assessment of equipment health. AI-driven methods detect deviations in temperature, vibration, current harmonics, and insulation behavior, allowing prediction of failures long before actual malfunction. The study highlights the system architecture, operational workflow, and industrial applicability while demonstrating improved reliability, reduced maintenance costs, and enhanced operational safety. Recent research confirms that AI monitoring solutions can reduce unplanned outages by up to 40% and increase equipment lifetime through proactive maintenance strategies. The findings suggest that AI-enabled monitoring represents a vital technological advancement for sustainable and intelligent electrical infrastructure management