摘要
为了从复杂的轴承振动信号中提取微弱的故障信息,将相关峭度引入滚动轴承故障诊断领域,结合奇异值分解(Singular Value Decomposition,SVD)和相关峭度,提出了一种新的滚动轴承故障特征提取方法。该方法首先利用SVD对轴承振动信号进行分解,然后根据相关峭度选取SVD分解后的分量,提取出滚动轴承的弱故障信号。通过对轴承内圈故障的仿真和实验研究验证了该方法的有效性。
In order to extract the faint fault information from bearing complicated vibration signals,the correlated kurtosis was introduced into the field of rolling bearing fault diagnosis.Combining SVD and the correlated kurtosis,a new feature extraction method for rolling bearing faults was proposed.According to this method,the bearing vibration signals were decomposed with the SVD method firstly,then with the correlated kurtosis the decomposed components were selected,from them the weak fault signals were extracted.The effectiveness of the method was demonstrated with both simulation and test results.
出处
《振动与冲击》
EI
CSCD
北大核心
2014年第11期167-171,共5页
Journal of Vibration and Shock
基金
国家自然科学基金资助项目(50979109)
国防重点预研基金资助项目(9140A27020413JB11076)
关键词
奇异值分解
相关峭度
滚动轴承
故障诊断
singular value decomposition （SVD）
correlated kurtosis
rolling bearing
fault diagnosis
作者简介
张永祥男，教授，博士生导师，1963年2月生
