Application of PCA-BP Neural Network Model in Tractor Engine Fault Diagnosis
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.
国家科技期刊平台
NSTL
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