Study on Neural Network Automobile Fault Diagnosis Expert System

Because of the product variety and structure complexity of automobile, the traditional diagnosis technologies were difficult to meet the requirements of fault detection and maintenance. In order to improve the diagnosis and maintenance level of automobile faults, the application of neural network technology in expert system of automobile fault diagnosis was studied. The basic concepts and methods of fault diagnosis expert system were introduced and neutral network model and BP algorithm were analyzed. The structure of neutral network expert system was brought forward, and the key technologies were discussed, including knowledge acquisition, knowledge representation and inference mechanism. Taking the case of abnormal noise of automobile engine, the typical fault phenomenon of abnormal sound were analyzed in detail, an automobile fault diagnosis expert system based on three-layer neutral network was designed and implemented. The experimental results showed that the sample output corresponded to the expected output within the error range, the system was reliable and the requirement of intelligent fault diagnosis was achieved.