基于机器学习算法的电气设备故障智能检测技术研究  

Research on Intelligent Fault Detection Technology of Electrical Equipment based on Machine Learning Algorithm

作　　者：吴昊 刘金南[1] Wu Hao;Liu Jinnan

机构地区：[1]江阴职业技术学院,江苏江阴214400

出　　处：《时代汽车》2024年第17期16-18,共3页Auto Time

基　　金：2023年度省高校优秀科技创新团队:“基于物联网的智慧电驱系统研发与产业化”科创团队

摘　　要：随着智能制造和工业自动化的不断推进,电气设备的稳定运行变得日益重要。然而,故障的不可预测性给设备维护带来了巨大挑战。因此,本文提出了一种基于主成分分析的电气设备故障智能检测技术,以提高故障诊断的准确性和效率。首先,分析了电气设备故障的常见类型和特征,以及影响故障检测准确性的关键因素。其次,提出一种基于主成分分析的电气设备故障智能检测技术,通过在线学习机制,使模型能够适应新的故障特征,实现持续优化和迭代更新。最后,为了验证所提出方法的有效性,研究构建了一个基于实际电气设备数据的测试平台。结果表明,所提出的故障智能检测方法能够有效识别复杂故障模式,并实现了对早期故障迹象的准确预警。With the continuous advancement of intelligent manufacturing and industrial automation,the stable operation of electrical equipment has become increasingly important.However,the unpredictable nature of failures poses a huge challenge for equipment maintenance.Therefore,this paper proposes an intelligent fault detection technology for electrical equipment based on principal component analysis to improve the accuracy and efficiency of fault diagnosis.Firstly,the common types and characteristics of electrical equipment faults are analyzed,as well as the key factors affecting the accuracy of fault detection.Secondly,an intelligent fault detection technology for electrical equipment based on principal component analysis is proposed,and the model can adapt to new fault characteristics through the online learning mechanism,so as to achieve continuous optimization and iterative update.Finally,in order to verify the effectiveness of the proposed method,a test platform based on actual electrical equipment data was constructed.The results show that the proposed fault intelligent detection method can effectively identify complex fault modes and achieve accurate early warning of early fault signs.

关 键 词：机器学习算法 电气设备 故障智能检测技术 主成分分析 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]