Methodology for on-line incipient fault detection in single-phase squirrel-cage induction motors using artificial neural networks
Methodology for on-line incipient fault detection in single-phase squirrel-cage induction motors using artificial neural networks
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DOI：
10.1109/60.84332
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发表时间：
1991-09
影响因子：
4.9
通讯作者：
M. Chow;S. Yee
中科院分区：
文献类型：
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作者：
M. Chow;S. Yee
提出了一种基于人工神经网络的鼠笼式异步电动机早期故障在线检测方法。在线早期故障检测器由两部分组成：（1）干扰和噪声过滤人工神经网络，以过滤掉瞬态测量值，而保留稳态测量值;（2）高阶早期故障检测人工神经网络，根据从电机收集的数据检测单相鼠笼式感应电动机的早期故障。仿真结果表明，神经网络在单相鼠笼型异步电动机早期故障的在线检测中取得了令人满意的性能。提出的神经网络故障检测方法不仅限于单相鼠笼式电动机（用作原型），而且还可以应用于许多其他类型的旋转机械，适当的修改。>
A novel approach for online detection of incipient faults in single-phase squirrel-cage induction motors through the use of artificial neural networks is presented. The online incipient fault detector is composed of two parts: (1) a disturbance and noise filter artificial neural network to filter out the transient measurements while retaining the steady-state measurements, and (2) a high-order incipient fault detection artificial neural network to detect incipient faults in single-phase squirrel-cage induction motors based on data collected from the motor. Simulation results show that neural networks yield satisfactory performance for online detection of incipient faults in single-phase squirrel-cage induction motors. The neural network fault detection methodology presented is not limited to single-phase squirrel-cage motors (used as a prototype), but can also be applied to many other types of rotating machines, with the appropriate modifications. >