Research On Fault Diagnosis Method For Motor Rolling Bearing Based On Vibration Signal Analysis

Motor is the main source of power in mechanical equipment,about 30% of the mechanical failure of the motor is caused by damage to the bearings.Once the bearing failure occurs,the machine will not be able to operate normally,bringing huge economic losses,or the machine will be destroyed,endangering the personal safety of site staff.Therefore,it is very necessary to carry out fault diagnosis of motor bearings.In order to detect motor bearing faults in a timely and effective manner,this paper studies the fault diagnosis algorithm of motor bearing based on vibration signal,which mainly includes weak signal enhancement and feature extraction algorithm.The specific research contents are as follows:(1)For the problem of selecting effective components after decomposition of the traditional CEEMDAN algorithm and the possible existence of a large amount of noise in these effective components,a multi-indicator joint screening criterion is introduced to make improvements to it.This criterion mainly selects the effective components containing fault information by the effective correlation cliff index,and determines whether these components need noise reduction treatment again due to the presence of large amount of noise by the arrangement entropy index,so as to realize the effective noise reduction of vibration signals.Through the analysis of the motor bearing failure simulation signal,it is verified that the improved CEEMDAN algorithm has better noise reduction performance,and based on this improved algorithm,the noise reduction process is carried out on the experimental data of motor bearings.(2)To address the problem that the fault characteristics are relatively weak when the motor bearing fails and are easily overwhelmed by a large amount of noise and cannot be extracted accurately,a fault diagnosis method based on the improved CEEMDAN-MOMEDA-Teager energy operator demodulation analysis is proposed.After noise reduction of the vibration signal by the improved CEEMDAN algorithm,the MOMEDA algorithm is used to enhance the fault impact components in the noisereduced signal,and the Teager energy operator operation is used to strengthen the fault characteristics,so as to realize the diagnosis of the motor bearing fault.Through the analysis of the motor bearing test data,the proposed method obtains the experimental analysis results with outstanding fault characteristics and obvious fault diagnosis effect.(3)A fault diagnosis method based on improved CEEMDAN-TFR mediation analysis is proposed for the problem that the features associated with the occurrence of faults in motor bearings are usually modulated on multiple frequency scales.And for the situation that the TFR method contains fault information on only some scales and the summation of complex wavelet coefficients can highlight the fault features,a 3 d B effective bandwidth selection criterion is introduced to select the effective information,so as to realize the improvement of the TFR demodulation method.Finally,by analyzing the experimental data of motor bearings,the obtained analysis results show that the fault features are obvious and easy to identify,which can effectively realize the diagnosis of bearing faults.(4)Design a rolling bearing vibration signal acquisition and analysis system based on Lab VIEW software.The system realizes the functions of acquiring,displaying and saving the vibration signals,and carries out time domain,frequency domain and time-frequency analysis on the acquired vibration signals.