Multi-technology Collaborative Fault Diagnosis and Intelligent Operation and Maintenance of Electrical Equipment

With the continuous advancement of intelligent manufacturing and industrial digital transformation, electrical automation equipment is increasingly developing toward integration, precision, and intelligence. The traditional labor-intensive and periodic-maintenance-based operation and maintenance (O M) approach can no longer meet the operational requirements of such equipment, resulting in frequent problems such as missed fault diagnosis, delayed response, and excessive maintenance. To address the industry pain points of inefficient O M and passive fault management in industrial electrical equipment, this study adopts a method combining theoretical review and engineering induction to systematically analyze the typical fault types and inducement mechanisms of automation equipment, examine the core shortcomings of traditional diagnosis and O M models, and integrate new diagnostic technologies including sensor monitoring, data analysis, infrared thermal imaging, and intelligent algorithms. An intelligent and refined O M system is constructed, with condition-based maintenance as the core, a data system as the support, and institutional and talent development as the guarantee. The study shows that intelligent sensing and data-driven condition prediction can effectively compensate for the deficiencies of traditional O M systems, realizing a transformation from passive emergency repair to proactive prediction, and providing a practical reference for the long-term O M of industrial electrical automation equipment.