**6. Conclusions**

Aiming at the problems existing in the BP neural network and convolutional neural network, the BP neural network optimization model and convolutional neural network model were established, respectively. They were applied to the fault diagnosis of the shifting hydraulic system of hydraulic continuously variable transmission. The results show the following:


For the follow-up research, there is still room for further improvement. In the optimization of the BP network, a better algorithm model can be proposed. In the selection of data sets, more complex and fault data can be collected, and the comprehensive verification can be carried out through the simulation and experimental data sets to improve the reliability of the algorithm. In the data feature extraction, more optimized feature extraction can be carried out to improve the accuracy of fault diagnosis.

**Author Contributions:** Conceptualization J.W., Z.L. and G.W.; formal analysis, Z.L. and G.W.; investigation, J.W., G.H. and S.Z.; resources, Z.L., S.Z. and M.X.; data curation, J.W. and G.W.; writing—original draft preparation, J.W.; writing—review and editing, J.W.; visualization, J.W.; supervision, Z.L., G.W., H.Z., G.H., S.Z. and M.X.; project administration, H.Z. and M.X.; funding acquisition, M.X. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work described in this paper was fully supported by the National Key R&D Program (2022YFD2001204), Basic Scientific Research Business Expenses of Central Universities (XUEKEN2022015), Jiangsu Agricultural Science and Technology Independent Innovation Fund (CX (22) 3101), Jiangsu International Science and Technology Cooperation Project (BZ2021007) and Jiangsu Modern Agricultural Machinery Equipment and Technology Demonstration and Promotion Project (NJ2021-06).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available upon request from the first author at (gerab.wang@jsafc.edu.cn).

**Conflicts of Interest:** The authors declare no conflict of interest.
