Motor Bearing Fault Diagnosis Based Hybrid Domain Feature Optimization
In order to solve the problem of high dimensionality of hybrid domain features and the existence of redundant features,which results in low accuracy of motor bearing fault diagnosis,a motor bearing fault diagnosis method based on hybrid domain feature preference is proposed.Firstly,the signal is processed by using the CEEMDAN(complete ensemble empirical model decomposition adaptive noise)to extract the entropy features of the first four IMF(intrinsic modal function)components obtained from the decomposition and the time and frequency domain features of the reconstructed signal to construct the hybrid domain feature set;then,the feature selection algorithm(mRMR-RF)combining mRMR(maximum correlation and minimum redundancy)and RF(random forest)is used to rank the importance of the extracted hybrid domain feature set to obtain the feature subset.Finally,the feature subset is input into the XGBoost(extreme gradient boosting)algorithm optimized using the GWO(grey wolf algorithm)for fault diagnosis,and the optimal feature subset is derived.The experimental results show that this method has fewer input features and higher fault diagnosis accuracy than single and mixed domain feature diagnosis methods.
motor bearingfault diagnosiscomplete ensemble empirical mode decomposition with adaptive noisefeature selectionextreme gradient boosting
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