The engine is the core component of the vehicle, timely and effective manner to discover and troubleshooting, reduce maintenance costs, reduce economic losses, increase the reliability of the engine at work to avoid accidents have great significance. Taking a model engine for the study, with testing techniques, signal processing, analysis, neural networks and fuzzy control theory, and come out adaptive fuzzy neural network fault diagnosis engine.The paper established a fault signal acquisition engine test stand and four kinds of artificial conditions, the vibration signal acquisition normal operating conditions and abnormal operating conditions by the acceleration sensor, and then using wavelet theory collected vibration signal de-noising process, improve signal to noise ratio and extract the fault characteristic value of the signal sample data as the network training and testing. Fuzzy neural network training and testing using sample data adaptive, that is pattern recognition engine failure, through simulation, and achieved good diagnostic results. Compared with the traditional BP Neural Network diagnostic methods, both in learning speed or accuracy of the diagnosis, Fuzzy Neural Network has more advantages in fault diagnosis.
