针对大型制造装备故障诊断中存在的高冲突证据问题,提出了一种改进的信息融合故障诊断方法.该算法通过各条证据可信度获得平均信任度,利用各证据的相对距离构造一个反映冲突强度的动态权重参数.为了使各条证据更能客观地反映装备的故障特性,利用模式之间的相似度获取证据的mass函数.仿真实验结果表明,该算法可以有效减少证据间的冲突,对大型制造设备故障诊断较高的识别率显示了该方法较好的实用价值.%For the problem of high conflicting evidence combination which exists in fault diagnosis of large manufacturing equipment, an improved fault diagnosis algorithm based on information fusion is proposed. To begin with, the algorithm tries to obtain average belief with the weighting factors based on the credibility of each evidence. With the relative distance of each evidence, a dynamic parameter is constructed, which can reflect the conflicting intensity of evidence, and then the algorithm obtains dynamic weight coefficients. Finally, iteration fusion is adopted with the weighted evidence according to D-S evidence theory. Meanwhile, in order to make the evidence reflecting the fault features of the equipment objectively, the mass functions of the evidence are acquired from the similarity among the patterns. Experimental result indicates this method can reduce the conflicts among evidences effectively, which has high recognition rate for large manufacturing equipment fault and shows good practical value.
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