摘要
轴承故障、定子绝缘故障与绕组股线断股、鼠笼转子断条等故障约占鼠笼异步电机故障的８０％。在本文和其姊妹篇［１］中，详细地讨了用模糊诊断方法对上述各类故障进行诊断的问题。本文将监测所得振动信号的时域方差和峭度，以及其振动信号的包络的幅频谱所得的几个特征频率处的幅度转化成隶属度向量，作为模糊诊断的输入向量，利用模糊诊断关系矩阵，得到异步电机的滚动轴承的几种故障的严重程度。本文还介绍了用最小二乘原理，对实验室一台７．５ｋＷ的鼠笼异步电机滚动轴承故障的隶属度函数、模糊诊断关系矩阵进行自学习和对该电机的滚动轴承各种故障诊断的结果。结果表明，本文所提供的方法是一种有效的方法。
The bearing failure, insulation failure and strand broken of stator winding, cage failure of rotor are the dominance part of cage induction motor failure. The paper and its companion paper discuss the diagnosis of this kind of failure mentioned above using fuzzy logic approach. The paper presents the forming of input vector of fuzzy inference which elements are the membership functions corresponding to the statistic characteristics (variance and kurtosis) obtained from the time domain wave of its vibration signal, and the vibration amplitude of bearing parts at their fault feature frequencies obtained by the envelop analysis of vibration signal measured which is based on the resonant demodulation principle. The the malfunction degree of several typical failures in the bearing can be learned. Additionally, by Principle of least square, the methods of computer self learning for membership functions and fuzzy relation matrixes are developed. The diagnosis software based on the fuzzy logic approach has been used for bearing fault diagnosis of a 7.5kW inductor motor, the results show that the fuzzy logic failure diagnosis is an effective approach.
出处
《电工电能新技术》
CSCD
1997年第1期30-34,共5页
Advanced Technology of Electrical Engineering and Energy
基金
国家科委国家攀登计划项目
关键词
故障诊断
模糊诊断
感应电动机
induction motor, fault diagnosis, fuzzy logic, rolling element bearing
