Study Of Intelligent Diagnosis About Electronic Control Engine Fault Based On Neural Network

With the rapid development of the auto industry, auto production and keep growing volume, and automobile manufacturing technology continues to progress, so that the automobile engine become increasingly complex. the standards III of emissions of motor vehicle will be implemented nationwide(equivalent to Euro III) , and mandating the installation of on-board diagnostic systems (OBD) in 2008.In modern society,it is payed more and more attention to the automotive gasoline engine fault diagnosis technology, if the automotive gasoline engine occur failure in certain areas but not in time to discover and remove, and the results will not only lead to damage to gasoline engine itself, and it might even cause a fatal car crash of the serious consequences. In recent years, China's auto industry has developed rapidly , so improve our lives, but because of more complex equipment and the number of vehicle become more , this is a problem to maintenance staff.It is practical significance to study the automotive gasoline engine intelligent fault diagnosis technology. And the safe operation of motor vehicles is more and more concerned,so enhance vehicle safety testing technology that would be important to study and solve the issue. Against such a backdrop, in this paper , traditional fault diagnosis expert system has knowledge acquisition bottleneck, and does not have the self-learning function, using artificial neural networks and fuzzy theory to study intelligent fault diagnosis electronic control gasoline engine. In allusion to idling or idling control valve failure, ignition coil failure , ignition timing is wrong, sparkplug fault ,throttle failure, leak into the trachea, the air filter failure, fault injector ,fuel supply system failure,cooling system failure, lubrication system failure of the electronic Control gasoline engine, I have designed BP diagnostic network and fuzzy BP diagnostic network. According to the characteristics of neural networks, indicated that neural network is feasible and inevitable for fault diagnosis. BP network is used for fault diagnosis for gasoline engine, simulation results show that BP network is a more practical neural network for fault diagnosis of electric control gasoline engine, its pattern recognition and classification is better. Because of complexity and ambiguity of diagnosis of electric control gasoline engine ,it is too rough and imprecise to adapt traditional two-valued logic based on Boolean algebra,therefore the concept of fuzzy logic is introduced, and fuzzy neural network is constructed, and it used to carry out fault diagnosis for electronic control gasoline engine. The simulation results show that fuzzy logic is used to neural networks, the expression of knowledge is more precise, it is not only more detailed description on the importation fault phenomenon, but also is a clear explanation for the reasons for the output fault, it is more in line with our thinking habits.