# Rotate Vector Reducer Fault Diagnosis Model Based on EEMD-MPA-KELM
## 5 Test and Data Analysis
### Simulation of EEMD-MPA-KELM Fault Classification Model  
MATLAB R2020b programming was used to select 1,680 monitoring data of pre-fault efficiency and post-fault efficiency as sample data for EEMD decomposition. Some graphs obtained are shown in Figures 8 and 9. It can be seen from Figures 8 and 9 that normal data and fault data are significantly different after EEMD decomposition. Since the energy of the signal is mainly concentrated in the lower IMF, the first six IMF components of each group are selected for average checking calculation. The two groups of data obtained 21 eigenvalues after collective average calculation. At this time, the eigenvalues were input into MPA-KELM classification model for training and classification. The decomposed IMF was taken as input, and the classification number was taken as output, among which classification 1 represented normal and classification 2 represented fault. The output is shown in Figures 10 and 11. Figure 8: IMF image after EEMD decomposition of normal data. **(a)** IMF1 component after EEMD decomposition of normal data; **(b)** IMF2 component after EEMD decomposition of normal data; **(c)** IMF3 component after EEMD decomposition of normal data; **(d)** IMF4 component after EEMD decomposition of normal data; **(e)** IMF5 component after EEMD decomposition of normal data; and **(f)** IMF6 component (residual component) after EEMD decomposition of normal data. Figure 10: Single operation result of EEMD-MPA-KELM model. (**a**) model training sets predict results; (**b**) model test sets predict results. Figure 9: Image of fault data after EEMD decomposition. (**a**) IMF1 component after EEMD decomposition of fault data; (**b**) IMF2 component after EEMD decomposition of fault data; (**c**) IMF3 component after EEMD decomposition of fault data; (**d**) IMF4 component after EEMD decomposition of fault data; (**e**) IMF5 component after EEMD decomposition of fault data; and (**f**) IMF6 component (residual component) after EEMD decomposition of fault data. It can be seen from Figure 10 that the EEMD-MPA-KELM diagnosis model has good recognition performance, and the overall recognition accuracy of its training set is relatively high, reaching 100%. The overall recognition accuracy of the test set is 90.33%. Figure 11 shows the visual confusion matrix of RV reducer fault diagnosis results based on EEMD-MPA-KELM. The diagonal elements in the matrix represent the correct predicted number of samples of a certain fault class, and the sum of data in each column represents the total number of samples of this fault class, and the sum of data in each row represents the total number of samples predicted for this fault class. Table 3 shows the evaluation indexes of the model. It can be seen from Figure 10 and Table 3 that the recall and accuracy rates of the proposed EEMD-MPA-KELM fault diagnosis model for various samples of running state are both higher than 85%, and the diagnostic accuracy rate for cutting machine cut-off and tide discharge detection rod plugging is up to 100%. The Kappa coefficient of the model as a whole is 0.82. The Classification accuracy is as high as 91.00%. The proposed RV reducer fault diagnosis model based on EEMD-MPA-KELM is verified to have high fault recognition sensitivity, high fault judgment reliability and strong overall fault-classification ability. In order to further verify the performance superiority of the proposed RV reducer fault diagnosis model based on EEMD-MPA-KELM, this model was compared and analyzed with the diagnostic results of other models. In order to eliminate the contingency of the test results, a total of 30 times were run, and the results are shown in Figure 12.  
Figure 11: Classification confusion matrix diagram of fault diagnosis.