**1. Introduction**

Hydro-mechanical continuously variable transmission (HMCVT) [1–3] is highly automated, and its shifting process is completely carried out under its transmission control unit (TCU) [4,5]. The fault of the position clutch or hydraulic control system will have a great impact on its shifting quality [6,7]. Therefore, in order to discover potential faults in time and improve the reliability of the shifting operation, the TCU needs to perform real-time fault monitoring. However, in the current fault diagnosis related to it, most of the research directions are mechanical traditional gearboxes, and few are specifically aimed at HMCVT. With the wide application of HMCVT, improving the reliability of the shifting process will become the direction of rapid development in the future [8,9].

The structure of the transmission system of HMCVT is complex, but overall it can be divided into mechanical systems and hydraulic systems. Mechanical system faults are mainly gear faults, and the current fault diagnosis methods for mechanical systems are relatively mature, such as wavelet analysis [10,11], support vector machine [12], hidden Markov model [13], etc. Hydraulic system faults include pump motor hydraulic system failures and clutch hydraulic system failures, which can be identified by analyzing pressure, flow, power and other data. Wang Guangming et al. studied gearbox speed ratio control and

**Citation:** Wang, J.; Lu, Z.; Wang, G.; Hussain, G.; Zhao, S.; Zhang, H.; Xiao, M. Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN. *Agriculture* **2023**, *13*, 461. https://doi.org/10.3390/ agriculture13020461

Academic Editor: Filipe Neves Dos Santos

Received: 12 January 2023 Revised: 3 February 2023 Accepted: 14 February 2023 Published: 15 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

proposed a hydraulic system fault diagnosis method based on the Fisher criterion kernel method for its clutch [14]; Grover Zurita et al. proposed a multi-channel deep support vector classification method for gearbox fault diagnosis [15]; Lin Ruilin et al. proposed the application of a robust residual support vector machine in fault diagnosis and realized the leakage fault diagnosis of the electro-hydraulic servo system [16] and Han Zhengze studied the fault diagnosis method of the rack rail hydraulic system, and constructed the fault diagnosis rules of the pilot system based on the fault tree method [17].

From the aspect of BP neural network optimization, particle swarm optimization (PSO), as a random search algorithm based on population, has been applied to BP neural network optimization because of its high accuracy and fast convergence. Zou Lan and others used the PSO algorithm to optimize the SOMBP neural network prediction model, and the recognition rate of the optimized model increased from 90% to 95% [18]. However, although the model recognition rate of the PSO algorithm's optimization has been greatly improved [19], the PSO algorithm also has some defects such as slow network convergence speed and it being easy to fall into the local optimum with the increase in iteration times, which means it is difficult to meet the use requirements. Therefore, PSO still has a lot of room for improvement [20,21].

In recent years, many scholars have conducted a series of studies on the intelligent diagnosis method of hydraulic system faults. Additionally, the BP neural network [22–24] and convolutional neural network [25,26] are popular among them. In order to make up for the shortcomings of previous research, the BP neural network optimization model and the convolutional neural network model are applied to the fault diagnosis of the HMCVT shift hydraulic system in this paper, and the classification results are compared.
