神经网络和证据理论融合的管道泄漏诊断方法

针对传统管道泄漏诊断方法存在的准确率不高的问题，结合无线传感器网络与信息融合技术，提出一种神经网络和证据理论有机结合的管道泄漏诊断方法. 在普通节点处建立两个子神经网络模型来简化网络结构，分别以负压波和声发射信号中的泄漏特征参数作为输入向量进行初始泄漏诊断；然后将神经网络的识别结果作为证据的基本概率分配，从而实现了赋值的客观化；采用改进的证据组合规则，在普通节点和汇聚节点处进行两级证据合成，充分利用了网络中各种冗余和互补的泄漏信息. 实验结果表明，该方法显著提高了管道泄漏诊断的准确率，降低了识别的不确定性.

A Pipeline Leakage Diagnosis For Fusing Neural Network and Evidence Theory

For reasons of low accuracy of traditional leakage, a pipeline leakage diagnosis method based on neural networks and evidence theory is presented by introducing wireless sensor networks and information fusion theory. Two sub-neural networks are established at normal node to simplify network structure. The leakage characteristic parameters of negative pressure wave and acoustic emission signals are used as input eigenvector respectively for primary diagnosis. Through making preliminary fusion results as the basic probability assignment of evidence, the impersonal valuations are realized. Finally, all evidences are aggregated at normal and sink node respectively by using the improved combination rules. The method makes full use of redundant and complementary leakage information. Numerical example shows that the proposed improves the leakage diagnosis accuracy and decreases the recognition uncertainty