拖拉机电气系统远程故障诊断模型研究  

Remote Fault Diagnosis Model of Tractor Electrical System

作　　者：陈艳茹 亓丹丹 张永学 高艳芳 Chen Yanru;Qi Dandan;Zhang Yongxue;Gao Yanfang(Huanghe Jiaotong University,Jiaozuo 454950,China)

机构地区：[1]黄河交通学院,河南焦作454950

出　　处：《农机化研究》2025年第3期242-246,253,共6页Journal of Agricultural Mechanization Research

基　　金：教育部产学合作协同育人项目(202101218021);河南省社科联调研课题(SKL-2021-2800)。

摘　　要：拖拉机正朝着智能化和自动化方向发展,而传统故障诊断方法的局限性日益突出,且受限于技术水平和知识储备的不足,通常依赖于人工观察和经验判断。为了进一步提高拖拉机故障诊断效率,采用了卷积神经网络模型,通过对拖拉机电气系统的传感器数据进行训练和学习,实现对故障的自动识别和分类。通过大量的数据样本和远程数据传输,该模型可以在远程环境下进行故障诊断,并给出相应的解决方案。试验结果表明:该模型在拖拉机电气系统故障诊断方面表现出较高的准确性和效率,故障诊断准确率达到98.96%,具有实际应用的潜力。研究结果旨在为拖拉机智能化和自动化发展提供一种新的故障诊断方法,并为农业机械维修提供重要的技术支持。Tractors are developing towards intelligence and automation,the limitations of traditional fault diagnosis methods are becoming increasingly prominent.Traditional fault diagnosis methods are limited by the lack of technical level and knowledge reserve,and usually rely on manual observation and experience judgment.To further improve the efficiency of tractor fault diagnosis,this study adopted a CNN neural network model to achieve automatic identification and classification of faults by training and learning from sensor data of tractor electrical systems.With a large number of data samples and remote data transmission,the model can perform fault diagnosis in a remote environment and give corresponding solutions.The test results showed that the model showed highly accuracy and efficiency in tractor electrical system fault diagnosis,with 98.96%fault diagnosis accuracy,and had potential for practical application.The research results aim to provide a new fault diagnosis method for the development of tractor intelligence and automation,and provide an important technical support for agricultural machinery maintenance.

关 键 词：拖拉机 电气系统 卷积神经网络 传感器 故障诊断 

分 类 号：S219.033[农业科学—农业机械化工程]