Study On Diesel Engine Fault Diagnosis Technology Based Multisensor Information Fusion

The engine as the representative of back and forth machine, the trouble demonstrates characteristics of complexity and multiplicity. It's difficult for us to discern the trouble by using traditional diagnosis method, and the diagnose accuracy is very low. With development and application on artificial intelligence technology, more and more researches applied to trouble diagnosis field and had acquired the result, which is better than traditional methods. In view of this, we introduce the technique of information fusion into the field of diesel engine fault diagnosis.This article is mainly about diesel engine fault diagnostic method. Based on analyzing the currently domestic and foreign studies, a new diagnostic method based on neural net and D-S inference is given and verified. Firstly, based on studying the traditional diagnostic methods and their characteristic, the study and application of new method is urgent required. Secondly, we discuss the network structure and the characteristic of BP neural network, and it shows that neural net can be used for diesel engine fault diagnosis. To the incertitude of fault diagnosis, the decision-making fusion based on D-S evidence theory is put forward. The basic conception of evidence theory is introduced, and with the simulation based on the example, we know evidence theory fusion improved the fault diagnosis precision. Finally, the method based on BP neural network and D-S inference is put forward. By the simulation of the example, the feasibility and availability of this diagnostic method are verified, which can increase the accuracy of fault diagnosis.