基于神经网络和证据理论的液压系统故障诊断

针对液压系统故障多样性和复杂性等特点,基于信息融合原理,提出了一种基于神经网络和D-S（Dempster-Shafer）证据理论相结合的液压系统故障诊断方法。该方法通过构建多子神经网络分类模块进行局部诊断,利用各子神经网络的输出值作为证据理论中的基本可信度,经过证据理论的再次融合得出最终的诊断结果。实例表明,该方法通过简化神经网络结构,提高了局部诊断网络的诊断能力,通过对多源多特征参数的融合,充分利用各传感器的冗余和互补的故障信息,与单一故障特征的诊断相比,显著提高了故障诊断的准确率,降低了决策的不确定性。

Hydraulic System Fault Diagnosis Based on Neural Network and Evidence Theory

According to the diversity and complexity features of hydraulic system fault,a hydraulic system failure diagnosis method combing neural networks and D-S evidence theory was presented by means of information fusion theory.This method conducts local diagnostic by building multi-neural network classification module,using the output of each neural networks as the evidence′s basis belief assignment,then through D-S evidence combination to get the final result.The example verifies that this method simplifies the neural network structure and improves the diagnostic capabilities of diagnostic networks,through combing the multi-source and multi-feature,the accuracy of fault diagnosis is improved significantly and the uncertainty of decision-making is reduced,when comparing with diagnostic based on single fault characteristic by making full use of various redundant and complementary information from multi-sensor.