摘要
机电系统的复杂化对设备故障的分类判型提出更高要求。据此提出一种基于小波包能量谱熵的机电故障分类方法。这种方法可以通过对不同故障信号对应的故障信息进行特征提取,以信号的小波包能量谱熵的提取原理为核心,结合支持向量机技术实现对不同故障特征的学习训练,进而实现对机电系统具体故障的分类识别。仿真结果证明其在不同机电系统故障情况下故障分类识别的效果,并为实际工程中机电系统的故障检测与分类识别提供了一种新思路。
As the electromechanical system becomes more complex,the requirements for classifying and judging equipment failures are becoming more and more stringent.Accordingly,an electromechanical fault classification method based on wavelet packet energy spectrum entropy is proposed.By extracting the fault information corresponding to different fault signals,taking the extraction principle of the wavelet packet energy spectrum entropy of the signal as the core,and combining the support vector machine technology,the learning and training of different fault characteristics is realized,therefore,the specific fault detection of the electromechanical system is achieved.The simulation results show the effect of fault classification and identification under different electromechanical system faults,providing a new idea for fault detection and classification identification of electromechanical systems,which can be used in practical engineering.
作者
崔科杰
竹小锋
蒋红辉
苟宇涛
CUI Ke-jie;ZHU Xiao-feng;JIANG Hong-hui;GOU Yu-tao(Zhejiang Zheneng Lanxi Power Generation Co.,Ltd.,Lanxi 321100,China;Hangzhou Quanxin Technology Co.,Ltd.,Hangzhou 310000,China)
出处
《浙江水利水电学院学报》
2022年第2期80-85,共6页
Journal of Zhejiang University of Water Resources and Electric Power
关键词
小波包能量谱熵
故障分类
特征提取
支持向量机
wavelet packet energy spectrum entropy
fault classification
feature extraction
support vector machine
作者简介
崔科杰(1982),男,浙江宁波人,工程师,硕士,研究方向为热工自动控制,E-mail:cuikj1982@163.com;通讯作者:苟宇涛(1997),男,硕士研究生,山西晋城人,研究方向为故障检测,E-mail:yutaog0928@163.com。
