发电厂设备故障预测及检修方法分析  

Analysis of Fault Prediction and Maintenance Methods for Power Plant Equipment

作　　者：吕春蕾 LV Chunlei

机构地区：[1]国网莱芜供电公司,山东济南250102

出　　处：《电力系统装备》2025年第1期129-130,185,共3页Electric Power System Equipment

摘　　要：发电厂设备的运行可靠性是确保能源供应的关键因素.文章以火电厂为研究对象,探讨了设备故障预测与检修的有效方法.通过统计分析方法分析了发电厂重要设备的故障数据,基于机器学习算法建立故障预测模型.在故障检修方面,文章比较了传统的定期检修与CBM策略,强调了CBM策略在延长设备使用寿命和减少非计划停机时间方面的优势.并通过实际案例分析,验证了预测模型和CBM策略在提高发电厂设备可靠性和经济效益方面的实际效用.研究结果不仅能为发电厂设备管理提供科学的决策支持,同时也为工业设备维护提供了新的思路和方法.With the development of industrial automation,the operational reliability of power plant equipment has become a key factor in ensuring energy supply.Taking thermal power plant as the research object,this paper discusses the effective methods of equipment fault prediction and maintenance.The fault data of important equipment in power plant is analyzed by statistical analysis method,and the fault prediction model is established based on machine learning algorithm.In terms of troubleshooting,the paper compares the traditional periodic maintenance with the CBM strategy,and emphasizes the advantages of the CBM strategy in extending the service life of the equipment and reducing unplanned downtime.The practical effectiveness of the prediction model and CBM strategy in improving the reliability and economic benefit of power plant equipment is verified through the case analysis.The research results can not only provide scientific decision support for power plant equipment management,but also provide new ideas and methods for industrial equipment maintenance.

关 键 词：故障预测 检修方法 发电厂 机器学习 条件基本维护 

分 类 号：TM62[电气工程—电力系统及自动化]