摘要
以列车轴承运行的振动加速度信号为研究对象,采用随机共振进行轴承振动信号的提取,同时基于主成分分析的方法实现对目前旋转机械常采用的23个混合域的故障特征参量进行分析,得到合适的轴承故障特征集.利用BP神经网络对以上内容的有效性进行验证.实验表明,结合2种方法得到的轴承故障诊断结果正确率可达到90%以上.
T his paper takes the train axle box bearing running vibration acceleration signal as the re‐search object ,studies the signal extraction and fault feature extraction ,which are the key aspects of real-time monitoring .To extract the effective bearing fault signal ,this paper carries out the research on the stochastic resonance method to extract the effective bearing fault signal ,and adopts principal component analysis （PCA） method to reduce the dimension of 23 mixed‐domain fault characteristic pa‐rameters and get the appropriate principal component characteristic parameters .Finally ,BP neural net‐work based bearing fault diagnosis system is designed to verify the validity of the research results of this thesis .The experimental results show that the accuracy of bearing fault diagnosis can reach more than 90% by combining the two methods .
出处
《武汉理工大学学报（交通科学与工程版）》
2015年第3期657-661,共5页
Journal of Wuhan University of Technology（Transportation Science & Engineering）
关键词
故障诊断
轴箱轴承
随机共振
主成分分析
fault diagnosis
rolling bearing
stochastic resonance
principal component analysis
