# Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning
## 4 Experimental Analysis
### Analytical Comparison
Here, the optimal parameters were 100 and 100. It was also found that the SVM classification effect of SVMD energy entropy was better than that of VMD energy entropy, whereas the SVM classification effect of the VMD fuzzy-entropy and the SVMD fuzzy-entropy classification effect was about the same. For the SVM and k-nearest neighbors (k-NN) [35] classification methods, in general, the classification accuracy of SVM is higher than that of k-NN. However, for smaller datasets, k-NN can obtain the same results as SVM or even better. In this paper's dataset, the number of samples was sourced from the same time series and the sample differences were small, so the results obtained by k-NN were slightly better than those obtained by SVM. In addition, k-NN and SVM can be applied without using a large amount of data for model training and can obtain better models quickly. Integrated learning is a good strategy to achieve learning by combining multiple learners, both on datasets with large and small amounts of data. The current integrated learning methods can be broadly classified into two categories: One is sequential integration  
Figure 12: Confusion matrix of SVM classification of gearbox working condition I energy entropy in the SEU dataset: (**a**) VMD; (**b**) SVMD. Figure 13: Confusion matrix of fuzzy-entropy SVM classification of gearbox working condition I in the SEU dataset: (**a**) VMD; (**b**) SVMD. methods, such as boosting in AdaBoost and gradient-boost decision tree (GBDT) [36]. The other is parallel integration methods, such as bagging and random forest (RF) [37]. The classification results of the energy-entropy and fuzzy-entropy sample sets obtained from the VMD and SVMD decompositions of the bearing data in the gearbox of SEU in the SVM, k-NN, RF, and GBDT models are shown in Table 5. The table shows that the accuracy of the SVMD fuzzy entropy k-NN, RF, and GBDT models in the gearbox working condition I data of SEU was the same as that of the VMD fuzzy-entropy RF and GBDT models, reaching 99.22%; the accuracy of the VMD fuzzy-entropy SVM and RF models in the working condition II data was the highest, reaching 99.61%. The SVMD feature-extraction (energy entropy, fuzzy entropy) machine learning in this paper was compared with the machine-learning classification results in the literature [38], as shown in Table 6. From the table, it can be seen that in the fault diagnosis of the bearing, the machine-learning classification effect of the SVMD method was better than the identification result of this data in the literature, and in the gearbox, the two were difficult to distinguish. In the paper, the grid-search method was also chosen to select the two model parameters of RF and GBDT. When performing the grid search, the n_estimators parameter and the max_depth parameter in RF were both set to integers between 5 and 13, the min_samples_split parameter was set to 2, and other parameters were set by default. In GBDT, the n_estimators parameter was set to an integer between 50 and 150 with an interval of 10, and other parameters were set by default.