Abnormal Vibration Fault Diagnosis of Reducer Based on Bayesian Network
In order to recognize the fault type of abnormal vibration of the reducer and reduce the cost of inspection and maintenance,an intelligent diagnosis model of abnormal vibration of the reducer is devel-oped.In the case of insufficient and unbalanced historical abnormal vibration data,a fault tree of the reducer is established by combing the historical fault history data.It is then mapped to the Bayesian network struc-ture of the abnormal vibration of the reducer.The historical abnormal vibration data are extracted and la-beled.The expectation maximization(EM)algorithm is selected as the parameter learning method to deter-mine the probability distribution of the node variables in the Bayesian network.After processing real-time vibration data,the model integrates the abnormal vibration feature discrimination mechanism of fault knowl-edge transformation and hierarchical Gibbs sampling algorithm to carry out fault probability inference for each node variable.Therefore,it can realize the timely location of abnormal vibration fault.Compared with other models,the proposed model has achieved great improvement in the accuracy of diagnosis results and distinguishing normal and abnormal data.The model is integrated into the intelligent operation and mainte-nance system of belt conveyor for engineering verification.
万方数据
维普
