摘要
针对故障轴承振动信号中含有强烈的背景噪声,难以提取故障频率的现实情况,提出了基于经验模态分解(Empirical Mode Decomposition,EMD)和奇异值差分谱的轴承故障诊断方法。首先通过EMD方法将非平稳的原始轴承振动信号分解成若干个平稳的本征模函数(Intrinsic Mode Function,IMF);由于背景噪声的影响,从各个IMF的频谱中难以准确地得到故障频率。对IMF分量构建Hankel矩阵并进行奇异值分解,进一步找到奇异值差分谱,根据奇异值差分谱理论对某个IMF分量进行消噪和重构,然后再求其频谱,便能准确地得到故障频率。实验结果表明,提出的方法能有效地应用于轴承的故障诊断。
In view of the strong background noise involved in the fault signals of rolling element bearings and the difficulty to obtain fault frequencies in practice, a fault diagnosis scheme, which is based on empirical mode decomposition （EMD） and dif- ference spectrum theory of singular value, is put forward in this paper. Firstly, original acceleration vibration signals are de- composed into a finite number of stationary intrinsic mode functions （IMFs） ; it is difficult to obtain fault frequencies because of strong background noise. Therefore, to identify the fault pattern, singular value features that extracted from a number of IMFs contain the most dominant fault information. To construct a Hankel matrix of an IMF and do a singular value decompo- sition （SVD）. To move forward a single step, difference spectrum of singular values is obtained. On the basis of difference spectrum theory, de-noising and reconstruction can be done to some IMF component in order to get its frequency spectrum. Finally, fault frequency can be identified accurately. Practical examples show that the diagnosis approach put forward in this paper can identify gear fault patterns effectively.
出处
《振动工程学报》
EI
CSCD
北大核心
2011年第5期539-545,共7页
Journal of Vibration Engineering
基金
国家863高技术研究发展计划资助项目(2006AA04Z402)
中央高校基本科研资金资助项目(JY10000904012)
关键词
轴承
故障诊断
经验模态分解
HANKEL矩阵
奇异值差分谱
bearing
fault diagnosis
empirical mode decomposition
Hankel matrix
difference spectrum of singular value
