摘要
为了提高异步电机故障诊断的可靠性,防止故障错报与漏报,以定子电流分析方法为理论基础,采取同时采集异步电机定子电流与电压信号的电机电气信号分析方法,分析异步电机电气、机械故障,并通过神经网络对复杂的电流和电压信号进行故障模式识别.模拟异步电机绕组匝间短路和断条故障进行实验,实验结果与实际基本符合.利用电气信号分析电机故障,可以区分电机电气故障和机械故障,为诊断结论提供了更加可靠的依据,与神经网络结合,利于故障模式的智能识别.
To improve the fault diagnosis reliability of an asynchronous motor and prevent misreporting and underreporting, the electrical signal analysis method was employed to analyze the electrical and mechanical faults based on stator current analysis theory and through simultaneously acquiri.ng the current and voltage signals of asynchronous motor. The fault model identification for complex current and voltage signals was conducted by neural networks. Both winding turn-to-turn short-circuit and broken rotor bar faults have been tested in laboratory, and the results are basically coincident with the real case. Electrical signals can be used to analyze and distinguish electrical and mechanical faults, which can provide reliable basis for diagnosis conclusion and is also benefidal to intelligent recognition of fault pattern with combining neural networks.
出处
《沈阳工业大学学报》
EI
CAS
2008年第3期241-244,265,共5页
Journal of Shenyang University of Technology
基金
教育部春晖计划资助项目(Z2005-1-21007)
关键词
故障诊断
电气信号分析
神经网络
异步电机
频谱分析
fault diagnosis
electrical signal analysis
neural networks
asynchronous motor
frequency spectrum analysis
