Hydraulic System Fault Diagnosis Based on Neural Network and Evidence Theory
Song Mingyuan · Journal of Taiyuan University of Science and Technology · 2012
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.