RESEARCH ON GEAR BOX FAULT DIAGNOSIS BASED ON DCNN AND XGBOOST ALGORITHM - ZHANG RongTao; CHEN ZhiGao; LI BinBin; JIAO Bin (Jixie qiangdu)
In order to solve the problem of complex fault diagnosis of gearbox,the DCNN（ Deep Convolution Neural Network） was combined with the XGBoost（ e Xtreme Gradient Boosting） algorithm to establish the fault diagnosis model. Firstly,the DCNN Model was used to adaptively extract the feature matrix of the original vibration acceleration signal. Secondly,the feature matrix was used as input data,and the parameters of XGBoost algorithm were adjusted by lattice parameter method,then the XGBoost model was obtained. Most after that,the XGBoost model was trained by the feature matrix,so the gear box fault diagnosis model of DCNN-XGBoost was obtained. In order to verify the validity of the model and the superiority of XGBoost algorithm,the model was compared with three models: DNN-BP（ Back Propagation neural network） model,DCNN-RF（ Random Forest） model and DCNN-SVM（ Support Vector Machine） model. The DCNN feature matrix and the artificial feature matrix were analyzed by t-SNE visualization algorithm,the results show that the visualization effect of DCNN feature matrix obtained is better than that of artificial feature matrix; Compared with XGBoost,the stability of Random Forest is not as good as that of XGBoost algorithm; Compared with BP neural network, XGBoost algorithm has some advantages in preventing over-fitting; The combination of SVM and DCNN has some limitations. Finally,the diagnostic accuracy and time of DCNN-XGBoost model is better than that of other models.