Bayesian Network-based Engine Fault Diagnosis

Engine as a typical reciprocating machinery and equipment, mechanical systems occupyan important position, but the complexity of its structure showing a diversity of the enginefailure, as a result of environmental noise, the accuracy of the information collection system,data processing and fault diagnosiseffects, resulting in engine failure diagnostic uncertainty inparticularly prominent, so to solve the uncertainty is the main contents of the current domesticand foreign scholars.Bayesian network based on Bayesian theory practice has accumulated at any time, onlinelearning to improve the network structure and parameters, to improve the fault diagnosiscapability, and probabilistic inference algorithm based on network structure, you can alwaysupdate the probability of information in the network. Be seen, the Bayesian network is verysuitable to solve the problem of complexity and uncertainty.This paper first describes the current situation of this subject at home and abroad, as wellas the feasibility of Bayesian networks to solve the engine fault diagnosis, followed by focuson the establishment of the Bayesian network, and particle swarm optimization algorithm isapplied to the Bayesian network parameter optimization, particle swarm optimization of theBayesian network structure.. We can get the vibration signal by diesel engine test, extract the eigenvalue of thevibration signal by Wavelet power spectrum. Training samples used to train Bayesiannetworks, and test results of the test samples on the training of the Bayesian network. Theconclusion of the Bayesian network for fault diagnosis of diesel engine, the contrastconcluded that the Bayesian network can be a good diesel engine fault diagnosis ofuncertainty. And found that this method can be diagnosed by the coexistence of multi-faulttype of engine failure, it also provides a direction later scholars of multiple faults coexist.