基于数据挖掘技术的拖拉机发动机故障诊断  

Tractor Engine Fault Diagnosis Based on Data Mining Technology

作　　者：匡伟祥[1] Kuang Weixiang(College of Modern Equipment Manufacturing,Chenzhou Vocational Technical College,Chenzhou 423000,China)

机构地区：[1]郴州职业技术学院现代装备制造学院,湖南郴州423000

出　　处：《农机化研究》2025年第2期244-248,共5页Journal of Agricultural Mechanization Research

基　　金：湖南省教育厅科学研究项目(21C0966,XJK20BZY019,ZJB2021004)。

摘　　要：拖拉机是农业生产的重要工具,发动机是其核心部件,如发动机出现故障,将会直接影响农业生产效率和产量。为此,提出了一种使用数据挖掘技术进行拖拉机发动机故障诊断的方法。利用了机器学习技术和统计学,首先针对拖拉机田间运行信号噪音较大的问题,引入小波阈值去噪的方法;其次,基于卷积神经网络模型,引入一种注意力机制,提高故障诊断准确率,并通过对拖拉机传感器数据进行分析,可以帮助诊断和预测发动机故障;最后,通过实验结果验证了算法的有效性。研究结果不仅可以提高故障的准确性和效率,还能够节约维修成本和提高机器的利用率,具有较高的应用价值。Tractor as an important tool for agricultural production,which,the engine is the core component of the whole machine,when the engine failure,will directly affect the efficiency and yield of agricultural production.Therefore,the timely diagnosis and repair of tractor engine faults is very important to ensure the smooth running of agricultural production.Therefore,this study proposes a method for tractor engine fault diagnosis using data mining techniques.The method utilizes the techniques of machine learning and statistics,firstly,it introduces a wavelet threshold denoising method for the problem of noisy tractor field operation signal,secondly,it introduces an attention mechanism based on convolutional neural network model to improve the fault diagnosis accuracy,and it can help to diagnose and predict the engine fault by analyzing the tractor sensor data.Finally,the effectiveness of the algorithm is verified by experimental results.The results of the study not only can improve the accuracy and efficiency of fault,but also can save the maintenance cost and improve the utilization rate of the machine,which has important practical significance and economic benefits.

关 键 词：拖拉机 发动机故障诊断 数据挖掘技术 机器学习 特征选择 

分 类 号：S219.03[农业科学—农业机械化工程] S126[农业科学—农业工程]