Abnormal Vibration Fault Diagnosis of Reducer Based on Bayesian Network

In order to recognize the fault type of reducer abnormal vibration and reduce the cost of inspection and maintenance, an intelligent diagnosis model is developed. In the case of insufficient historical abnormal vibration data, a fault tree of the reducer is established by combing the historical fault data. It is then mapped to the Bayesian network structure. The expectation maximization (EM) algorithm is selected as the parameter learning method to determine the probability distribution of the node variables. After processing real-time vibration data, the model integrates the abnormal vibration feature discrimination mechanism and hierarchical Gibbs sampling algorithm to carry out fault probability inference. 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 maintenance system of belt conveyor for engineering verification.