Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN

Jiabo Wang, Zhixiong Lu, Guangming Wang, Ghulam Hussain, Shanhu Zhao, Haijun Zhang and Maohua Xiao ()
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Jiabo Wang: College of Engineering, Nanjing Agricultural University, No. 40 Dianjiangtai Road, Pukou District, Nanjing 210031, China
Zhixiong Lu: College of Engineering, Nanjing Agricultural University, No. 40 Dianjiangtai Road, Pukou District, Nanjing 210031, China
Guangming Wang: College of Mechanical and Electronic Engineering, Shandong Agricultural University, No. 61 Daizong Street, Taishan District, Taian 271018, China
Ghulam Hussain: Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Tarbela Road, District Swabi, Khyber Pakhtoon Khwa, Topi 23460, Pakistan
Shanhu Zhao: Jiangsu Yueda Intelligent Agricultural Equipment Co., Ltd., No. 9 Nenjiang Road, Economic and Technological Development Zone, Yancheng 224100, China
Haijun Zhang: College of Engineering, Nanjing Agricultural University, No. 40 Dianjiangtai Road, Pukou District, Nanjing 210031, China
Maohua Xiao: College of Engineering, Nanjing Agricultural University, No. 40 Dianjiangtai Road, Pukou District, Nanjing 210031, China

Agriculture, 2023, vol. 13, issue 2, 1-17

Abstract: There are some problems in the shifting process of hydraulic CVT, such as irregularity, low stability and high failure rate. In this paper, the BP neural network and convolutional neural network are used for fault diagnosis of the HMCVT hydraulic system. Firstly, through experiments, 120 groups of pressure and flow data under normal and four typical fault modes were obtained and preprocessed; they were divided into 80 groups of training samples and 40 groups of test samples via random extraction, using the BP neural network model and convolutional neural network model for fault classification. The results show that compared with BP, PSO-BP and other models, the fault diagnosis rate of the BAS-BP neural network model can reach 92.5%, and the average diagnosis accuracy rate of the convolutional neural network can reach 97.5%, which can be effectively applied to the fault diagnosis of the HMCVT hydraulic system and provide some reference for the shifting reliability of hydraulic CVT.

Keywords: HMCVT; fault diagnosis; BP algorithm; CNN; attribute reduction (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2023:i:2:p:461-:d:1069886

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