Research on Bearing Fault Diagnosis Based on Belief Rule Base with Attribute Reliability
Bearing fault diagnosis is a key issue in the health management of rotating instruments.However,in engineering practice,the ob-servation data of bearings may be affected by some interference factors,including sensor quality and environmental noise.In traditional belief rule bases(BRB),the model inference assumes that the input data is completely reliable,but unreliable observation data can reduce the accu-racy of BRB.The Belief Rule Base Model with Attribute Reliability(BRB-r)provides a modeling framework and analysis method,and is an expert system that can aggregate unreliable quantitative data and expert knowledge.To improve the accuracy of bearing fault diagnosis,a new bearing fault diagnosis model based on BRB-r is proposed.Firstly,calculate the reliability of attributes based on statistical methods;Then,use evidence reasoning as the inference engine of the model;Finally,the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model parameters.The verification experiment results show that BRB-r can to some extent eliminate the influence of uncertain information in observation data and effectively process unreliable data,with good diagnostic performance.
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维普
