Journal of Electrical Engineering and Automation

As a key equipment in power generation, the operation status of the thermal power generation boiler directly affects the stability and efficiency of the power system. This study aims to explore the fault prediction method based on modern data analysis technology and the optimized maintenance strategy, so as to improve the safety and economy of boiler operation. Currently, thermal power generation boilers face challenges in fault prediction, including complex data processing, inaccurate prediction models and other issues. An intelligent fault prediction system integrating multiple algorithms is proposed, and the effectiveness and reliability of the system are verified through a case study. Based on the fault prediction results, a targeted optimization scheme for the maintenance strategy is proposed, emphasizing the importance of preventive maintenance. Through scientific and reasonable maintenance strategies and accurate fault prediction, the failure rate of the boiler can be significantly reduced, and the power generation efficiency can be improved.

Thermal power generation boilers; Fault prediction; Data analysis; Maintenance strategy optimization