Health diagnosis of coal mine belt conveyor based on improved fuzzy information fusion
In order to solve the problem of paradox when mining belt conveyor uses multi-sensor information fusion to deal with conflicting evidence,an improved fuzzy information fusion method based on similarity matrix and information entropy is proposed.The membership function in fuzzy set theory is introduced to obtain the basic probability assignment provided by each sensor.By the amount of conflicting evidence,the similarity matrix of mutual support between the evidences is calculated.In order to determine the trust coefficient of each evidence,the trust coefficient is weighted average,then the information entropy function is used to modify the conflict evidence,and a new conflict evidence processing method is established.Finally the rule of evidence theory synthesis is applied to achieve fuzzy information fusion.It shows that the method is effective by algorithm comparison,and this method can effectively identify the health diagnosis status of belt conveyor and can avoid misdiagnosis.
coal mine belt conveyorfuzzy information fusionsimilarity matrixinformation entropyhealth diagnosis
万方数据
维普
