【摘要】 针对旋转机械故障诊断的不确定性问题,提出一种基于证据理论的数据融合故障诊断方法,把5种无量纲免疫检测器的敏感因子和信息因子通过D-S联合规则联合多个证据组形成一个新的综合证据组,建立多故障特征信息融合诊断框架,充分利用不同证据体的冗余和互补故障信息,通过对不同轴承故障进行分析,结果表明,此方法能有效地减少诊断的不确定性,提高故障诊断的准确性。更多还原

【Abstract】 Since rotating machinery fault diagnosis problem of uncertainty,a method of data fusion fault diagnosis based on Dempster-sharfer evidential theory is proposed.Sensitivity factors and information factors of five non-dimensional immune detectors are combined by united rule and formed a new comprehensive evidence group.Multi-fault characteristic information fusion diagnosis frame is constructed,in which the redundancy and complementary information of fault diagnosis are utilized fully.The fault analysis show the proposed way can effectively reduce uncertainty and improve accuracy of fault diagnosis.更多还原