基于特征权值小波包能量分析的异步电动机电气故障特征提取  

Electrical fault feature extraction of asynchronous motor based on feature weight-wavelet packet energy analysis

作　　者：郭昱君 王爱元[1,2] 姚晓东 GUO Yujun;WANG Aiyuan;YAO Xiaodong(School of Electrical Engineering,Shanghai Dianji University,Shanghai 201306,China;Foshan Gaoming Minge New Type Motor Electronic Control Research Institute,Foshan 528500,Guangdong,China)

机构地区：[1]上海电机学院电气学院,上海201306 [2]佛山市高明区明戈新型电机电控研究院,广东佛山528500

出　　处：《上海电机学院学报》2022年第3期142-148,共7页Journal of Shanghai Dianji University

摘　　要：异步电动机是生产生活中应用最广泛的电动机。对异步电动机电气故障特征进行提取,是保证电动机正常运行的重要手段。首先,对异步电动机电气故障进行仿真与模拟后,获得定子电流信号。然后,通过小波包能量分解和重构这些信号,得到故障特征向量。最后,运用特征权值算法得出每个特征的权重。研究表明:借助特征权值算法与小波能量分析的方法能使提取的故障特征更为精确,仿真验证了其有效性和可行性。Asynchronous motor is the most widely used motor in production and life.It is an important method to extract the electrical fault characteristics of asynchronous motor to ensure the normal operation of motor.First,after the electrical fault of asynchronous motor is simulated,the stator current signal is obtained.Then,through a wavelet packet energy decomposition and reconstruction of these signals,the fault feature vector is obtained.Finally,the weight of each feature is obtained by using the feature weight algorithm.The research shows that the extracted fault features can be more accurate by the feature weight algorithm and wavelet energy analysis,and its effectiveness and feasibility are verified by simulation.

关 键 词：异步电动机 电气故障 小波包能量分析 特征权值 

分 类 号：TM343[电气工程—电机]