Feature Classification Method of Frequency Cepstrum Coefficient Based On Weighted Extreme Gradient Boosting

Cepstrum coefficient and decision tree classification provide a feasible method for mechanical fault diagnosis. However, this method has the problems of low accuracy of feature extraction and low reliability of feature classification. The efficiency and classification results of this method are not ideal. Therefore, this paper proposes a feature classification method of frequency cepstrum coefficients based on weighted extreme gradient boosting. The proposed method is mainly divided into two parts: feature extraction and feature classification. In the part of feature extraction, Considering the limitation of Mel frequency scale in MFCC, an adaptive frequency cepstrum coefficient (AFCC) method is proposed, which can adaptively obtain the frequency scale of the corresponding signal, thus improving the feature accuracy extracted by MFCC method. In the aspect of feature classification, XGboost algorithm has strong learning ability, which easily leads to over fitting and affects the classification accuracy, A weighted extreme gradient boosting (WXGB) method is proposed, and an improved distance evaluation method (IDE) is introduced to modify the feature weights learned by XGboost, so as to improve the prediction accuracy of the classification model. In order to verify the effectiveness of the method, the vibration signals of rolling bearings with different fault types are taken as samples to carry out feature extraction and classification. The results of feature extraction and classification of different methods are compared, and the effectiveness of the proposed feature classification method based on weighted extremum gradient boosting frequency cepstrum coefficient in rolling bearing fault diagnosis is verified.