摘要
当前,国内核电厂的板式热交换器的运维策略主要以传统的定期维修为主,这种维修方式不仅耗费较多的维修资源,还容易引起过度维修或维修不足等问题,进而降低板式热交换器设备的可靠性和可用性。本文将结合国内外先进的状态监测技术和预测性维修(PdM)技术,深入研究基于传热学机理和机器学习算法的故障预测融合模型及其在预测性维修中的应用,为板式热交换器的运维策略从定期维修向基于状态的维修转变,提供一套切实可行的技术方案,以此提高核电厂热交换器的安全性、可靠性和经济性。
At present,the operation and maintenance of plate heat exchangers in domestic nuclear power plants mainly rely on traditional periodic maintenance.This not only consumes a large amount of maintenance resources but also easily leads to over-maintenance or insufficient maintenance problems,reducing the reliability and availability of equipment.This paper integrates advanced condition monitoring and predictive maintenance(PdM)technologies at home and abroad,deeply explores the fault prediction fusion model based on heat transfer mechanism and machine learning algorithm and its application in predictive maintenance,provides a feasible technical solution for the transformation of plate heat exchangers from periodic maintenance to condition-based maintenance,and improves the safety,reliability,and economy of heat exchangers in nuclear power plants.
作者
黄华奇
杨中卿
张圣
HUANG Hua-qi;YANG Zhong-qing;ZHANG Sheng(Equipment Reliability Technology Center of Suzhou Nuclear Power Research Institute Co.,Ltd.,Shenzhen 518000,China;National Nuclear Power Plant Safety and Reliability Engineering Technology Research Center,Suzhou 215004,China)
出处
《价值工程》
2025年第26期137-141,共5页
Value Engineering
关键词
板式热交换器
状态监测
预测性维修
故障预测
机器学习
plate heat exchanger
condition monitoring
predictive maintenance
fault prediction
machine learning
