Based On Bayesian Network Research Of Motor Fault Diagnosis Method

Along with the development of science and technology, electrical equipment is widely used in various fields, therefore, it also put forward higher to motor fault diagnosis technology. In the field of the motor fault diagnosis, many scholars use wavelet analysis, artificial neural network, the expert system for the motor fault diagnosis system, but because the motor structure is complex, and the relationship of each parts is quite closely, therefore,motor fault diagnosis system usually exist unascertained information, and how to properly and accurately deal with these uncertain information, it is required to constantly perfect and improve in the field of the motor fault diagnosis.For the uncertain information in the field of the motor fault diagnosis system, it uses D-S evidence theory of the information fusion technology to analyze and fusion processing the collected motor fault characteristics signal. D-S evidence theory provides a strong method for the uncertain information expression and synthesis. And then, it uses the Bayesian network to diagnose the signal of motor fault features, until the motor fault is reasoned. Bayesian network is a directed acyclic graph, it can use a conditional probability distribution directly express the dependencies between variables. At the same time, it use Bayesian network that is based on the Bayesian method for motor fault diagnosis, so that it can make the result of the diagnostic reasoning more accurately and reasonably.Bayesian network is a directed acyclic graph, through a conditional probability distribution directly express the dependencies between variables. According to the working principle and its faults motor structure characteristics, and combined with bayesian network and decision tree of their respective characteristics, puts forward a bayesian network and decision tree of combining new methods, namely the decision tree-bayesian network model for motor fault diagnosis.This paper expounds the decision tree and bayesian network basic ideas and relevant algorithm, by decision tree-bayesian network to get all the conversion of the algorithm of the fault between nodes of conditional probability distribution, quickly and accurately that final motor fault diagnosis results. Effective to solve the motor fault diagnosis in uncertainty information, which can be obtained more accurate, efficient diagnosis. The experimental results show that the sample with the rising number of, motor fault diagnosis accuracy improve continuously, to achieve the expected goal.