摘要
以共轨柴油车定性特征参数与定量特征参数为输入变量,构建了5层架构的模糊神经网络智能诊断模型,提出了基于置信度D-S理论二级融合的故障诊断模式,给出了网络的学习过程以及训练方式。通过仿真推理表明,该智能诊断系统以特征信号为融合的故障诊断模式更能准确定位故障,表明该系统研究的可行性。
In the common rail diesel car qualitative characteristic parameters and the quantitative parameters as the input variables,building five layer structure of fuzzy neural network intelligent diagnosis model is proposed,based on reliability theory D-S two level information fusion fault diagnosis model,gives the network learning and training method.Through the simulation of reasoning suggests that,the intelligent fault diagnosis system with characteristic signal for fusion fault diagnosis model can more accurately locate fault,suggests that the development of the feasibility of the system.
出处
《农机化研究》
北大核心
2013年第11期218-222,共5页
Journal of Agricultural Mechanization Research
基金
浙江省大学生科技创新(省新苗人才计划)项目(2011R446003)
关键词
共轨柴油车
模糊神经网络
专家系统
故障诊断
fuzzy neural network
common rail diesel car
expert system
fault diagnosis
