基于机器学习的电厂设备故障诊断系统研究  

Research on Equipment Fault Diagnosis System in Power Plant Based on Machine Learning

作　　者：薛薇 XUE Wei(Datong Vocational and Technical College of Coal,Datong,Shanxi Province,037000 China)

机构地区：[1]大同煤炭职业技术学院,山西大同037000

出　　处：《科技资讯》2024年第18期90-92,共3页Science & Technology Information

摘　　要：针对电厂设备故障诊断问题进行了基于机器学习方法的研究。利用支持向量机(Support Vector Machine,SVM)和卷积神经网络(Convolutional Neural Network,CNN)等技术,开发了一种高效的故障诊断系统。研究首先进行了数据的采集与预处理,然后通过特征选择和提取,构建并优化了机器学习模型。实验结果表明:此诊断系统在故障诊断的准确率、召回率和F1值等方面均达到了95%以上的指标,显著优于传统方法。研究的创新点在于引入了深度学习技术,并结合电厂设备的实际运行数据,提升了诊断系统的可靠性和实时性。研究成果为电厂设备的故障诊断提供了一种有效的解决方案,具有重要的应用价值。In view of the problem of power plant equipment fault diagnosis,research based on Machine Learning method is conducted in this article.An efficient fault diagnosis system is developed by using technologies such as Support Vector Machine(SVM)and Convolutional Neural Network(CNN).In the research,data collection and preprocessing are first carried out,and then the machine learning model is constructed and optimized through fea-ture selection and extraction.Experimental results show that the system has reached more than 95%indicators in terms of fault diagnosis accuracy,recall rate and F1 value,which is significantly better than traditional methods.The innovation of the research lies in the introduction of Deep Learning technology,combined with actual operating data of power plant equipment,to improve the reliability and real-time performance of the diagnostic system.The research results provide an effective solution for fault diagnosis of power plant equipment and have important appli-cation value.

关 键 词：机器学习 故障诊断 支持向量机 卷积神经网络 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]