Study On Pattern Recognition Of Automobile Electrical Control System Faults By Neural Networks

Thesis introduces the process of the equipment developing and analyses the basic theory of Automobile Fault Diagnosis (AFD). According to the characteristics of the Automobile Electrical Control System Fault Diagnosis (AECSFD) and the demand of the Fault Pattern Recognition (FPR), the principles of selection and the extract on automobile technology condition features and the main pattern recognition methods are put forward. The typical faults of automobile electrical control system are taken for example, and their technology condition feature are describe. Artificial Neural Networks (ANN) is used for the fault pattern recognition, which the fault reason determine and the fault positions analyses. Because the construction of electrical control system is complex, the symptoms of fault have the basic feature that one fault is resulted from many reasons. Therefore, the basic fault pattern is established by the fault symptoms and Neural Network is trained by the sample or sample composed of the single fault pattern, so that it can discern the reasons and positions of the different faults. MATLAB programs are used for the ANN training and to recognize the reasons of the unstable idle speed on the electrical control engine.