摘要
高速列车牵引电机轴承故障特征微弱且干扰噪声强,为解决电机轴承故障特征难以提取的问题,文章提出了一种基于谱负熵信息图与最大相关峭度解卷积(MCKD)的电机轴承故障诊断方法。首先,对故障轴承振动信号进行基于谱负熵的信息图处理,确定最佳中心频带和带宽,从而对轴承振动信号进行带通滤波;然后,对滤波后的信号采用MCKD方法进行故障特征增强;最后,对故障特征增强后的信号进行包络分析,识别出电机轴承的故障特征。经仿真信号和台架试验数据验证,结果表明,信息图-MCKD方法对牵引电机轴承故障诊断具有良好效果。
A motor bearing fault diagnosis method based on spectral negative entropy information graph and Maximum Correlation Kurtosis Deconvolution(MCKD) is proposed to solve the problem of weak fault characteristics and strong interference noise in high-speed train traction motor bearings.Firstly,the vibration signal of the faulty bearing is processed using an information graph based on spectral negative entropy to determine the optimal central frequency band and bandwidth,thereby performing bandpass filtering on the bearing vibration signal;Then,the filtered signal is enhanced with fault features using the MCKD method;Finally,envelope analysis is performed on the signal with enhanced fault features to identify the fault characteristics of the motor bearings.Verified by simulation signals and bench test data,it is shown that the information graph MCKD method has good effectiveness in diagnosing faults in traction motor bearings.
作者
杨岗
杨惠心
张东兴
YANG Gang;YANG Huixin;ZHANG Dongxing(School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China;School of Mechanical Engineering and Automation,Northeastern University,Shenyang 110013,China)
出处
《铁道车辆》
2024年第4期140-148,共9页
Rolling Stock
基金
国家重点研发计划(2020YFB1200300ZL)
四川省重点研发项目(2023YFG0063)。
关键词
高速列车
牵引电机轴承
故障诊断
谱负熵
信息图
MCKD
high-speed train
bearing of traction motor
fault diagnosis
spectral negative entropy
infogram
MCKD
