# Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning
## 6 Conclusions  
An effective signal-decomposition method is an important tool for mechanical-fault diagnosis. The original vibration signal contains a lot of information. By decomposing the original signal into individual components and analyzing each of them, it is easy to find the faults present in the machine. This paper used an iterative SVD method that decomposes the signal into its constituent components, which is a successive implementation of VMD. It can also be seen as an extension of the VME method. This method was built by adding some criteria to the VMD algorithm to distinguish it from the previous one. The advantage of SVMD over VMD is that it does not need to know the number of modes available in the signal and converges to almost the same mode pattern as VMD with a known number of modes \(k\). The performance was even better than VMD in some cases.  
The energy entropy and fuzzy entropy of each component were obtained by decomposing the vibration signals under two working conditions of SEU gears by VMD and SVMD. Each of them was constructed into the energy-entropy sample sets and fuzzy-entropy sample sets, which were trained and tested in the established machine-learning classification models SVM, k-NN, RF, and GBDT. The results show that in SEU gearbox working condition I, the SVMD energy entropy, the fuzzy entropy in SVM and k-NN models, and the classification accuracy of VMD energy entropy were above 90%. The classification accuracy of the VMD energy entropy was poor. Whereas in integrated learning, the classification accuracy of VMD and SVMD fuzzy entropy and energy entropy were not much different, in SEU gearbox case II, the classification accuracy of SVMD energy entropy and fuzzy entropy was the highest in SVM and k-NN classification. The accuracy was close to 90%. The classification accuracy of VMD was even worse compared with SVMD. The same phenomenon occurred in integrated learning, as well.  
Finally, the SVMD feature-extraction diagnosis results of this paper for the gearbox data of SEU were compared with the results of other literature The overall effect was better than the results of the method in the literature, which verifies the effectiveness of the SVMD feature-extraction method.  
Conceptualization, L.Z.; funding acquisition, L.Z.; investigation, Y.Z. and G.L.; methodology, L.Z., Y.Z., and G.L.; software, G.L.; supervision, L.Z.; validation, Y.Z. and G.L.; visualization, G.L.; writing--original draft, L.Z. and G.L.; writing--review and editing, L.Z. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) of China (No. 311021013), the National Natural Science Foundation of China (No. 51775037), and the Fundamental Research Funds for Central Universities of China (No. FRF-BD-18-001A).  
The SEU datasets that support the findings of this study are openly available online at: [[https://github.com/cathysiyu/Mechanical-datasets/tree/master/gearbox/gearest](https://github.com/cathysiyu/Mechanical-datasets/tree/master/gearbox/gearest)]([https://github.com/cathysiyu/Mechanical-datasets/tree/master/gearbox/gearest](https://github.com/cathysiyu/Mechanical-datasets/tree/master/gearbox/gearest)) (accessed on 1 July 2022).  
The authors thank the large-scale scientific facility, Materials Service Safety Assessment Facilities (MSAF), for support of the simulation and data platform. The authors would like to thank [PERSON] for the editing of the English language and style in the revised manuscript.  
The authors declare no conflict of interest.