Research for Fault Diagnosis Method and System for Diesel Engine Based on ANFIS

Research for Fault Diagnosis Method and System for Diesel Engine Based on ANFIS

After compliance verification, operating vehicles can enter the road transportation market. Diesel engine is the main power source of these vehicles, there will be some typical faults during the use of diesel engine, which will affect the technical status of vehicles. According to the fault diagnosis problem of diesel engine, a fault diagnosis method based on Adaptive-Network-Based Fuzzy Inference System(ANFIS) was proposed, Subtractive clustering algorithm was used to confirm the original structure of fuzzy inference model, and ANFIS was used to build an original fault diagnosis model of diesel engine. Hybrid algorithm is used to train the parameter of fuzzy rule, and the final model is established. Simulation experiment results show that the modeling algorithm based on subtractive clustering-ANFIS is effective. It has been found that the average error is 7%, the recognition accuracy is 93.33%. Simulation results show that the fitting ability, convergence speed and recognition accuracy of ANFIS model are all superior to back propagation neural networks (BPNN), and much more suitable as diesel engine fault diagnosis model. Finally, an effective fault diagnosis system is developed by using the given method.