摘要
为解决建筑电气系统故障诊断和监测中高度依赖人工巡查和检测,自动化程度低下导致故障诊断滞后的问题,有必要研究以智能化的监测方法或手段诊断出故障位置,达到高效和经济的目的。本文以实际工程案例为依托,在研究建筑电气故障事故的监测基础上,以此为机器学习样本,提出基于BP神经网络法和ELM机器极限学习机法的建筑电气故障诊断方法。研究结果可为新建建筑或者老旧小区改造的建筑电气故障诊断和监测提供方法和案例。
In order to solve the problem that the fault diagnosis and monitoring of building electrical system is highly dependent on manual inspection and detection,and the low degree of automation leads to the lag of fault diagnosis,it is necessary to study the intelligent monitoring method or means to diagnose the fault location,so as to achieve the purpose of high efficiency and economy.Based on the actual engineering case,on the basis of studying the monitoring of building electrical fault accidents,and taking this as the machine learning sample,this paper puts forward the building electrical fault diagnosis method based on BP neural network method and elm machine limit learning machine method.The research results can provide methods and cases for building electrical fault diagnosis and monitoring of new buildings or reconstruction of old residential areas.
作者
马曙光
Ma Shuguang(Hohhot Construction Engineering Quality and Safety Center,Hohhot 010020,China)
出处
《科学技术创新》
2022年第15期177-180,共4页
Scientific and Technological Innovation
关键词
故障诊断
BP神经网络法
建筑电气
监测方法
ELM极限学习机法
Fault diagnosis
BP neural network method
Building electrical
Monitoring methods
Elm limit learning machine method
