摘要
为了解决滚动轴承振动信号中微弱故障信息难以提取的问题,提出了一种基于奇异值分解(Singular Value Decomposition,SVD)和Teager-Kaiser能量算子(Teager-Kaiser Energy Operator,TKEO)的轴承振动信号特征提取方法。采用SVD将突变信息从背景噪声和光滑信号中分离,提取信号的突变信息;利用TKEO计算突变信息的瞬时能量,对该能量信号进行频谱分析,从而提取出轴承振动信号的能量频谱特征,用于故障检测。将该方法应用于轴承外圈、内圈局部故障状态下的振动信号特征提取,利用特征信息能够准确检测并识别出故障类型,表明了该方法的可行性和有效性。
In order to solve the difficult problem in extracting the weak fault information from rolling bearing vibration signal, a method of feature extraction of bearing vibration signal based on Singular Value Decomposition（SVD）and Teager-Kaiser Energy Operator（TKEO）is proposed. The abrupt information is extracted from satin signal and noise by using the SVD. It calculates the instantaneous energy of abrupt information by using TKEO, and the energy information is analyzed to gain the feature of the energy frequency spectrum of bearing vibration signal that used for fault detection. The method is used to extract the fault feature of bearing vibration signal with outer and inner circle faults, and the fault type can be accurately detected and identified by feature information. The result shows that the method presented here is feasi-ble and valid.
出处
《计算机工程与应用》
CSCD
2014年第17期195-199,共5页
Computer Engineering and Applications
基金
国家自然科学基金(No.51169007)
云南省科技计划项目(No.2012CA022
No.2011DA005
No.2010DH004)
云南省中青年学术和技术带头人后备人才培养计划项目(No.2011CI017)
