摘要
文中针对列车走行部中轴承的故障设计了一种故障监测系统,实时监测轴承的状态,提高轴承故障诊断的精度。该系统将数据采集与处理分离,对轴承的温度和振动数据进行采集,传给处理板进行诊断,使用谱峭度提取振动信号中的共振频率,以此确定复Morlet小波的中心频率和尺度,使用小波变换对信号进行处理。该算法能自适应提取信号的共振频率,有效过滤外界噪声的干扰,提高轴承故障诊断的精度。通过模拟轴承故障对该系统进行验证,该系统能有效检测走行部轴承的故障。
In this paper, a fault monitoring system was designed for the bearing fault in the running gear of train, which can monitor the bearing status in real time and improve the accuracy of bearing fault diagnosis.The system separated data acquisition and processing, collected the temperature and vibration data of the bearing, transmitted it to the processing board for diagnosis, used the spectral kurtosis to extract the resonance frequency in the vibration signal, and determined the center frequency and scale of the complex Morlet wavelet.The signal was processed using wavelet transform.The algorithm can adaptively extract the resonance frequency of the signal, effectively filter the interference of external noise, and improve the accuracy of bearing fault diagnosis.The system was verified by simulating bearing faults, and the system can effectively detect the faults of running gear bearings.
作者
李俊曹
伍川辉
李恒奎
LI Jun-cao;WU Chuan-hui;LI Heng-kui(School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China;CRRC Qingdao Sifang Co.,Ltd.,Qingdao 266111,China)
出处
《仪表技术与传感器》
CSCD
北大核心
2022年第8期63-68,共6页
Instrument Technique and Sensor
基金
国家重点研发计划资助项目(2020YFB1200300ZL)。
关键词
监测系统
故障诊断
谱峭度
复Morlet小波
monitoring system
fault diagnosis
spectral kurtosis
complex Morlet wavelet
