农机化研究2025，Vol.47Issue(3)：254-258,5.DOI:10.13427/j.issn.1003-188X.2025.03.040
PCA-BP神经网络模型在拖拉机发动机故障诊断中的应用
Application of PCA-BP Neural Network Model in Tractor Engine Fault Diagnosis
摘要
Abstract
Tractor engine fault diagnosis is to identify the type and location of engine faults by analyzing and processing the information of tractor engine operation status and sensor data,and to diagnose tractor engine faults timely and accu-rately,which is of great significance to improve the efficiency and economic benefits of agricultural equipment use.In this study,the sensor data of tractor engine were processed by dimensionality reduction based on principal component a-nalysis(PCA)algorithm,and then the reduced data were classified and identified using BP neural network to achieve the diagnosis of tractor engine faults.The experimental results showed that the PCA-BP neural network model can accu-rately diagnose multiple faults of tractor engines,and had higher accuracy and better generalization ability than the tradi-tional BP neural network model.The research results showed that the PCA-BP neural network model had greater appli-cation prospects in tractor engine fault diagnosis.关键词
拖拉机发动机/故障诊断/主成分分析/BP神经网络Key words
tractor engine/fault diagnosis/principal component analysis/BP neural network分类
农业科技引用本文复制引用
杨健..PCA-BP神经网络模型在拖拉机发动机故障诊断中的应用[J].农机化研究,2025,47(3):254-258,5.基金项目
省级教育体制机制改革试点项目(G5-28) （G5-28）
四川省2018-2020年高等教育人才培养质量和教学改革重大项目(JG2018-1101) （JG2018-1101）
四川省产教融合示范项目(18) （18）
