Design and Application of Automobile Engine Fault Diagnosis Teaching Experimental Platform Based on BP Algorithm

In order to enable teachers and students to better understand the fault diagnosis of automobile engines, this paper designs and develops an experimental platform for automobile engine fault diagnosis. The BP neural network is used to perform multi-level identification of vehicle engines under different working conditions. The model can effectively identify common engine faults, and use the BP neural network to train various faults to ensure high prediction accuracy for various faults. The experiment shows that the model proposed in this paper diagnoses diesel engine faults with an accuracy rate of 92.5 %, which is about 15 % higher than the conventional diagnosis method. Finally, by comparing with other machine learning methods (such as SVM), the superiority of BP neural network in processing complex data is verified. The implementation of this project will open new ways for the teaching and research of automobile engine fault diagnosis courses, and lay a theoretical and technical foundation for its application in engineering.