测试科学与仪器2016，Vol.7Issue(4)：368-374,7.DOI:10.3969/j.issn.1674-8042.2016.04.010
神经网络与 D-S 证据理论融合的液压系统故障诊断方法
Fault diagnosis method of hydraulic system based on fusion of neural network and D-S evidence theory
刘保杰 1杨清文 1吴翔1
作者信息
- 1. 陆军军官学院 五系，安徽 合肥 230031
- 折叠
摘要
Abstract
According to fault type diversity and fault information uncertainty problem of the hydraulic driven rocket launcher servo system(HDRLSS),the fault diagnosis method based on the evidence theory and neural network ensemble is proposed.In order to overcome the shortcomings of the single neural network,two improved neural network models are set up at the com-mon nodes to simplify the network structure.The initial fault diagnosis is based on the iron spectrum data and the pressure, flow and temperature(PFT)characteristic parameters as the input vectors of the two improved neural network models,and the diagnosis result is taken as the basic probability distribution of the evidence theory.Then the objectivity of assignment is real-ized.The initial diagnosis results of two improved neural networks are fused by D-S evidence theory.The experimental results show that this method can avoid the misdiagnosis of neural network recognition and improve the accuracy of the fault diagnosis of HDRLSS.关键词
多传感器信息融合/故障诊断/D-S 证据理论/BP 神经网络Key words
multi sensor information fusion/fault diagnosis/D-S evidence theory/BP neural network分类
信息技术与安全科学引用本文复制引用
刘保杰,杨清文,吴翔..神经网络与 D-S 证据理论融合的液压系统故障诊断方法[J].测试科学与仪器,2016,7(4):368-374,7.
