In order to diagnose gear shifting process in automated manual transmission(AMT),a semi-quantitative signed directed graph(SDG)model is applied.Mathematical models are built by analysis of the power train dynamic ...In order to diagnose gear shifting process in automated manual transmission(AMT),a semi-quantitative signed directed graph(SDG)model is applied.Mathematical models are built by analysis of the power train dynamic and the gear shifting control process.The SDG model is built based on related priori knowledge.By calculating the fuzzy membership degree of each compatible passway and its possible fault source,we get the possibilities of failure for each possible fault source.We begin with the nodes with the maximum possibility of failure in order to find the failed part.The diagnosis example shows that it is feasible to use the semi-quantitative SDG model for fault diagnosis of the gear shifting process in AMT.展开更多
为降低电网故障诊断中因人为主观因素的影响而造成的误差,提出了一种基于BP与分层变迁的加权模糊Petri网(weighted fuzzy Petri net,WFPN)相融合的输电线路故障诊断方法。根据故障信息确定出可疑元件,然后针对各元件分别建立它们的子模...为降低电网故障诊断中因人为主观因素的影响而造成的误差,提出了一种基于BP与分层变迁的加权模糊Petri网(weighted fuzzy Petri net,WFPN)相融合的输电线路故障诊断方法。根据故障信息确定出可疑元件,然后针对各元件分别建立它们的子模型和综合诊断模型。考虑到Petri网模型与BP神经网络在结构和形式上有一定的相似性,因此本文采用BP算法对Petri网模型中的权值进行训练。仿真结果表明该方法具有合理性及有效性。展开更多
基金Supported by the Basic Research Foundation of Beijing Institute of Technology(20130342035)
文摘In order to diagnose gear shifting process in automated manual transmission(AMT),a semi-quantitative signed directed graph(SDG)model is applied.Mathematical models are built by analysis of the power train dynamic and the gear shifting control process.The SDG model is built based on related priori knowledge.By calculating the fuzzy membership degree of each compatible passway and its possible fault source,we get the possibilities of failure for each possible fault source.We begin with the nodes with the maximum possibility of failure in order to find the failed part.The diagnosis example shows that it is feasible to use the semi-quantitative SDG model for fault diagnosis of the gear shifting process in AMT.
文摘为降低电网故障诊断中因人为主观因素的影响而造成的误差,提出了一种基于BP与分层变迁的加权模糊Petri网(weighted fuzzy Petri net,WFPN)相融合的输电线路故障诊断方法。根据故障信息确定出可疑元件,然后针对各元件分别建立它们的子模型和综合诊断模型。考虑到Petri网模型与BP神经网络在结构和形式上有一定的相似性,因此本文采用BP算法对Petri网模型中的权值进行训练。仿真结果表明该方法具有合理性及有效性。