Research on Failure Characteristics of Electric Logistics Vehicle Powertrain Gearbox Based on Current Signal

Author:

Tang Qian12,Shu Xiong13ORCID,Wang Jiande1,Yuan Kainan4,Zhang Ming3,Zhou Honguang1

Affiliation:

1. Hunan Provincial Key Laboratory of Vehicle Power and Transmission System, Hunan Institute of Engineering, Xiangtan 411104, China

2. New Energy Vehicle Research Center, Hunan Institute of Engineering, Xiangtan 411104, China

3. Department of Technology Research and Development, Xiangtan South Locomotive Manufacturing Co., Ltd., Xiangtan 411104, China

4. China Machinery International Engineering Design & Research Institute Co., Ltd., Changsha 410019, China

Abstract

As a core component of the powertrain system of Electric Logistics Vehicles (ELVs), the gearbox is crucial for ensuring the reliability and stability of ELV operations. Traditional fault diagnosis methods for gearboxes primarily rely on the analysis of vibration signals during operation. This paper presents research on diagnosing gear tooth wear faults in ELV powertrains using motor current signals. Firstly, an experimental test platform was constructed based on the structural principle of the powertrain of ELV models. Subsequently, a pure electric light truck powertrain gearbox with tooth wear was tested. Time–frequency domain analysis, amplitude analysis, ANOVA analysis, kurtosis analysis, and zero−crossing points analysis were used to analyze the U−phase current of the motor connected to the gearbox to study the characteristics of the phase current of the drive motor after tooth wear. The results indicate that while the time–frequency domain characteristics of the U−phase currents are not significantly altered by tooth wear faults, the amplitude, variance, and kurtosis of the current increase with the severity of the wear. Conversely, the number of zero−crossing points decreases. These findings provide valuable insights into new methodologies for diagnosing faults in ELV powertrain systems, potentially enhancing the efficiency and effectiveness of troubleshooting processes.

Funder

Science and Technology Foundation of Hunan Province

National Key Research and Development Foundation

Publisher

MDPI AG

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