# Fault Diagnosis of Planetary Gear Train Crack Based on DC-DRSN
## 5 Conclusions  
In order to solve the problems of weak feature extraction ability and low fault diagnosis accuracy of existing planetary gearboxes in noisy environments, a fault diagnosis method based on DC-DRSN is proposed to realize the fault monitoring of planetary gear train gears. With the help of the bearing public dataset of CWRU, the proposed method is compared with the traditional DRSN, and the diagnostic accuracy of the proposed method is higher than that of the traditional DRSN. On this basis, the method is applied to the proportionally reduced planetary gear train crack fault test data of a nuclear power gearbox, and the planetary gear train crack fault diagnosis is realized, with an accuracy of 97.22%. The results of this paper are as follows:  
1. A one-dimensional and two-dimensional parallel residual contraction network structure is constructed, which not only retains the temporal sequence and correlation of one-dimensional signals but also includes the information expression ability contained in two-dimensional images. In this way, more feature information is extracted, and the diagnostic performance of the model is improved.
2. Combined with the diagnostic characteristics under different noises, the number of residual shrinkage modules is reasonably determined. Thanks to the soft thresholding in the module, each sample has its own unique set of thresholds, which can be adapted to fault diagnosis in different noise environments.
3. The accuracy of the proposed method in diagnosing crack faults under different loads of planetary gear train gearboxes is tested. The results show that only a few samples are misclassified, which verifies that the proposed method has good expression and generalization ability.  
[PERSON] conceived this research; [PERSON] performed the experiments and data processing, and wrote the original draft preparation; editing and review on this article was completed by [PERSON]; [PERSON] is responsible for overseeing the progress and visualization of the article. All authors have read and agreed to the published version of the manuscript.  
This research was founded by the National Natural Science Foundation of China (No.52075062), Chongqing Municipal Science and Technology Bureau Innovation and Development Joint Fund Project (CSTB2022 NSCQ-IZX0024), special key project of technological innovation and application development in Chongqing (cstc2020 jscx-dxwtBX0048) and Graduate Innovation Project of Chongqing University of Technology (No. gzlcc20243005).  
Not applicable.  
**Data Availability Statement**: Publicly available bearing datasets were analyzed in this study. This data can be found here: [[https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)]([https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)). The raw data of gear supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Testing Accuracy** \\ \hline DC-DRSN & 97.22\% \\ DRSN & 96.67\% \\ VGG-16 & 95.49\% \\ GoogleNet & 96.10\% \\ Alexnet-18 & 96.24\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The accuracy of the gear test set under different deep learning models.