Fault Diagnosis System Of Engine Based On Virtual Instrument

Engine is a complex structure and have many moving parts and interference excitation system, which usually include initiating systems, fuel supply system, exhaust systems, lubricating system and cooling system, some engine subsystems includes pressurization subsystem and electronic control subsystem, these subsystem may produce fault in the operation process, which cause serious threats on the traffic safety. Lack of system fault detection means both at home and abroad.Based on study and comparing with the various diagnosis technologies, the neural network theory and virtual instrument technology are applied to EFI engine fault self-diagnosis. The results show that the reliability of the engine running is improved and the significant economic benefits are received by guaranteeing the engine performance, reducing spare parts and shortening maintenance time.Firstly, the basic principle, the model structure and the algorithm design of BP neural network are introduced in this paper; some improved algorithms to BP neural network and its training effect are studied either.Through the system simulation results prove the feasibility of the BP neural network for the fault diagnosis.Then analysis the composition of the engine signal and explain the fault phenomena corresponding the sensors and the signal processing based on the LabVIEW. a universal platform for the intelligent fault diagnosis system is designed with the research on parameters which characterize EFI engine performance. The platform combines with signal acquisition technology, signal processing technology, database technology and other intelligent fault self-diagnosis technology including the new fault diagnosis model in this paper.Finally, taking the emission test as an example, design a engine emission auto-diagnosis subsystem based on the neural network and virtual instrument technology and verify the accuracy of the system, This subsystem overcomes many difficulties such as the data acquisition and storage, the signal analyzing and diagnostic reasoning of neural network. Besides, the subsystem also achieves analysis functions on-line, improves the diagnostic accuracy and speed up the development progress of the system.