Research On Wind Turbine Vibration Signal Acquisition Analyzer And Diagnosis Method

There are many faults in the operation of the wind turbine,which can be avoided by fault diagnosis and condition monitoring.In view of the particularity of the wind turbine vibration signal acquisition,this paper developed a wind turbine vibration signal acquisition analyzer based on LabVIEW platform.The system has automatic recording and other functions which can be well applied to the vibration signal acquisition of the wind turbine in the field.The test results show the effectiveness of the system.In addition,the rolling bearing plays an important role in the wind turbine,and it has important practical significance to research on the fault diagnosis of the wind turbine.In this paper,the rolling bearing fault diagnosis method is studied,the main contents are as follows:The empirical wavelet transform(EWT)is difficult to extract the fault feature of bearing weak fault in strong noisy environment.A new rolling bearing weak fault diagnosis method based on probabilistic principal component analysis(PPCA)and EWT is proposed.This method is effective to restrain noise interference and extract signal fault feature,and it is better than the pure EWT and envelope analysis method.In view of the problem that the two parameter in PPCA algorithm needs to be chosen artificially,a new method named adaptive probabilistic principal component analysis(APPCA)is proposed to enhance feature of bearing fault.In order to adaptively achieve the best analysis result,the particle swarm optimization algorithm with multi-parameter optimization characteristic is applied to search for the optimal combination of influencing parameters of PPCA based on the maximum kurtosis criterion.The simulation and test results show the effectiveness of this method.