基于MOMEDA与Teager能量算子的滚动轴承故障诊断  

Fault diagnosis of rolling bearings based on the MOMEDA and Teager energy operator

作　　者：祝小彦 王永杰 ZHU Xiaoyan;WANG Yongjie(School of Energy Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China)

机构地区：[1]华北电力大学能源动力与机械工程学院,河北保定071003

出　　处：《振动与冲击》2018年第6期104-110,123,共8页Journal of Vibration and Shock

摘　　要：滚动轴承早期故障信号中原始冲击成分容易被强噪声淹没,故障特征提取难度较大。针对这一问题,提出了多点最优调整的最小熵解卷积(MOMEDA)与Teager能量算子相结合的滚动轴承故障诊断方法。利用MOMEDA算法对原始故障信号进行滤波处理,通过Teager能量算子增强解卷积信号中的冲击特征,对信号进行包络分析。通过对比包络谱中的主导频率与滚动轴承的故障特征频率判断故障位置,实现轴承的故障诊断。仿真数据与试验数据分析结果表明,该方法能够有效提取故障信号中的特征信息,具有一定的实用性。The original impulse components in rolling bearing incipient fault signals are difficult to be extracted since they are always covered by strong noise.Aiming at this problem,a new fault diagnosis method for rolling bearing incipient faults based on the multipoint optimal minimum entropy deconvolution adjusted(MOMEDA)and Teager energy operator was proposed.Firstly,an original fault signal was filtered by MOMEDA then the Teager energy operator was used to enhance the deconvolution signal.Finally,in order to find the fault frequency,the envelope analysis was employed to deal with the processed signal.The fault location of the rolling bearing was extracted by contrasting the major frequency with the fault frequency of the rolling bearing,therefore,the fault type of the rolling bearing was confirmed.The analysis results of simulated signals and measured signals show that the proposed method is able to extract fault impulse signals and it is kind to practicability.

关 键 词：MOMEDA Teager 故障诊断 滚动轴承 特征提取 

分 类 号：TH133.3[机械工程—机械制造及自动化] TH17