A fault diagnosis method of wind turbine bearings based on an enhanced morphological filter
QI Yongsheng1,2, FAN Ji1,2, LI Yongting1,2, GAO Xuejin3, LIU Liqiang1,2
1.Institute of Electric Power, Inner Mongolia University of Technology, Huhhot 010080, China;
2.Laboratory of Electrical and Mechanical Control, Hohhot 010051, China;
3.Faculty of Information, Beijing University of Technology, Beijing 100124, China
Abstract:The weak fault-related features of vibration signal, which originates from wind turbine rolling element bearings, are generally immersed in environmental noise and harmonic interference and difficult to extract.This issue is addressed in this paper by proposing a new enhanced morphological filtering scheme for fault diagnosis.Firstly, a new morphology analysis method, named morphological comprehensive filter-hat transform (MCFH) , was constructed to extract fault-related impulses from measured signal in strong background noise.And its filtering property was investigated by the nonlinear filter frequency response characteristics, which provides a theoretical basis for the application of fault-related impulses extraction.Secondly, an adaptive scale selection strategy was explored to obtain appropriate filter scale for MCFH.Thirdly, an improved envelope derivative energy operator was utilized to enhance the impulse characteristics of the signal after morphological filtering and to suppress the frequency of in-band noise.In the both simulation and experimental studies for wind turbine bearing, the proposed method delivered better fault feature extraction and noise reduction performance than the traditional methods.
齐咏生1,2,樊佶1,2,李永亭1,2,高学金3,刘利强1,2. 基于增强型形态学滤波的风电机组轴承故障诊断方法[J]. 振动与冲击, 2021, 40(4): 212-220.
QI Yongsheng1,2, FAN Ji1,2, LI Yongting1,2, GAO Xuejin3, LIU Liqiang1,2. A fault diagnosis method of wind turbine bearings based on an enhanced morphological filter. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(4): 212-220.
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