中文摘要：

证据理论在合成高度冲突的证据时,得到的结果往往有悖常理。几种代表性的改进方法虽然能较好地解决此问题,但收敛速度较慢,并且利用这些改进方法组合一致证据时发散,从而限制证据理论在故障诊断领域的应用。鉴于此,提出一种基于证据可信度的证据合成新方法,并结合神经网络,提出基于改进证据理论的多神经网络融合故障分类方法。以齿轮为研究对象,将齿轮原始特征参数空间划分为多个子空间,建立各子空间对应的神经网络诊断模型,将各子神经网络的输出作为证据体,以所提出的改进证据合成方法对各证据体进行组合实现故障模式的分类识别。将所提方法与传统证据理论方法、其他代表性改进方法以及传统神经网络方法的分类结果进行对比,验证改进证据合成方法融合分类的有效性。

英文摘要：

Combination results of the evidence theory will be out of accord when the evidences highly conflict with the real condition, some improved methods can solve this problem, but the convergence speed is rather slow. Furthermore, the combination of these improved methods will diverge when the evidences are consistent, thus limiting the application of evidence theory in condition monitoring system. In view of this, a novel evidence combination approach based on evidence confidence is proposed, and a multiple neural network fusion model is constructed for fault classification on the basis of this improved evidence combination method. A case of gear fault diagnosis using the proposed model is studied. The fault feature space is divided into several subspaces, and the corresponding sub-neural network classifiers are established. The output of these sub-neural network classifiers are used as the combination evidences. Finally, different faults are classified through combining the obtained evidences by the novel combination method. Comparing with the classification results of traditional evidence theory method, other representative improved methods and traditional neural network method, experiment results indicate the effectiveness of this improved evidence fusion method.