Research Of Engine Fault Diagnosis System Based On Data Fusion

With the development of China's economic and transport, cars have being widely used in many fields. It has become one of the marks of modern society. However, as the complex structures of engine, poor working conditions, making the failure rate high and maintenance costs much. It is therefore necessary to find simple, practical and effective engine fault diagnosis method.This paper is writen under the premise of in reading large information about fault diagnosis combining the engine fault characteristics. Trying to use the method comparison - multi-sensor data fusion which is quite popular in the field of of control in recent years to develop a fault diagnosis system used in the automotive engine. Through theoretical analysis and simulation results show that the data fusion methode of fuzzy logic and neural networks applied in the engine fault diagnosis can get better results. In order to achieve better results, on the basis this paper design a new method-FNNC, and use it in the training of the system, simulation experiments show that it is an efficient and effective way, have a good futhure in theory and application.This paper includes the content:(1) Introduction of fault diagnosis in particular, the automobile engine fault diagnosis technology and the status;(2) Introduction of multi-sensor data fusion technology development and the status, focused on fuzzy logic and neural network;(3) The combination of engine failures in the characteristics and features of data fusion method and design a fault diagnosis system,give a new method (FNNC)and look forward to improve the Accuracy rate;(4) Simulating the fault diagnosis system for engine fault diagnosis with MATLAB, and give the test results;(5) Summary and Outlook.