摘要
为了有效检测交流电机定子绕组匝间短路故障,应用多源信息融合理论将交流电机定子电压、电流负序分量通过李萨如方法进行融合,形成负序李萨如图形,提取并建立图形特征值与多个故障特征量之间的数学关系。通过基于模型的仿真分析及故障电机试验,对负序李萨如图形中可以反映故障的特征值的变化规律进行研究。在此基础之上,论证了负序李萨如图形倾角作为故障特征分量进行电机定子绕组匝间短路故障诊断的鲁棒性及准确性,从而形成图形化识别的故障诊断方法,为电机及其拖动系统的故障诊断提供新的思路和方法。
In order to detect stator winding inter-turn short circuit faults of AC motors, voltage and current negative components were synthesized into Lissajous figures by Lissajous method based on the multi-information fusion theory. Functional relationships between graphic features and fault features were deduced by mathematical analysis. Dynamic changing regularities of graphic features which can reflect the fault of motors were analyzed by way of simulations and fault motor experiments. Then the robustness and accuracy of the negative Lissajous figure angle being as the fault feature component was demonstrated. Finally a figure recognition method for fault diagnosis was formed. The method in this paper can provide new thinking and method for fault diagnosis of motors and driving systems.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2013年第15期119-123,8,共5页
Proceedings of the CSEE
基金
国家自然科学基金项目(51275375)
陕西省教育厅资助项目(12JK0682)~~
关键词
信息融合
负序李萨如图形
异步电机
定子匝间短路
故障诊断
information fusion
negative Lissajous figure
induction motor
stator winding inter-turn short circuit
fault diagnosis
