Solusion Of Bayesian Network For Uncertain Problem Of Motor Fault Diagnosis

Uncertain problems are key problems in fault diagnosis fields, which are resulted by many important reasons, including complex diagnosis objects, limit test means, and the inexact diagnosis knowledge, etc. Motor is large mechaatronics equipment, which has complex, correlative relation exiting in its units, and the units are full of uncertain factors and information. So the fault of motor is possible multi-kinds, correlative. In the all, the most important problem of solving motor fault diagnosis is to solve the uncertain problem.There are many common methods for solving uncertain problems, such as Bayesian method, fuzzy theory, DS theory, certain theory etc., And there are many experts researching on these methods, and has obtained that, Bayesian network based on Bayesian theory is the best method for solving uncertain problems now.In this paper, for solving uncertain problems, the fault diagnosis structure model and function fusion model was proposed, the models were based on the information fusion technology and the research of uncertain problems in fault diagnosis process, and the diagnosis object was Audi A6 1.8L motor. Bayesian network fusion algorithm was proposed for the knowledge expression and constitution of the models, which were researched deeply.Construct of Bayesian network included structure learning and parameter learning. There are many methods for structure learning. According to the information obtained by sensors timely, an improved structure learning method of Bayesian network was proposed in order to get more exact diagnosis result. The on-line learning idea was adopted for the construct of structure, and the learned structure should be close to the former structure, the MDL of learned structure should be less than the former structure. Then the data from sensors inputting into the system continuously, the new structure was obtained by learning algorithm continuously. For getting parameters of Bayesian network, MLE method was adopted, which is adapt to solving uncertain problems.