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首页> 外文期刊>Proceedings of the National Science Council, Republic of China, Part A. Physical Science and Engineering >Application of artificial neural network on sound-signal recognition for induction motor
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Application of artificial neural network on sound-signal recognition for induction motor

机译：人工神经网络在感应电动机声信号识别中的应用

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摘要

In the past, the research on fault recognition for induction motors only concentrated on spectrum amplitudes which are based on a constant load. However, the frequency and amplitude of the spectrum analyzed under different fault conditions are also affected significantly by load variation. Hence, simply using spectrum amplitudes to recognize motor faults is not sufficient in a practical system. Both various load conditions and different types of faults will influence the spectrum structure. In order to recognize faults under various load conditions, we have to consider band shift and amplitude as two major factors. In this paper, we use band shift and amplitude techniques to solve the spectrum problem under various load conditions and different types of faults. We also use the methods of frequency axis adjustment and feature exaction to solve the band shift and amplitude variation problems respectively. After the above-mentioned procedures, efficient features are obtained. Then, we use the back propagation artificial neural network (ANN) to train and recognize fault conditions. In addition, we compare the recognition ability between the artificial neural network and traditional method. All the theories and methods used in the paper are validated by means of different experimental results on motors.
机译：过去，感应电动机故障识别的研究仅集中在基于恒定负载的频谱振幅上。但是，在不同故障条件下分析的频谱的频率和幅度也会受到负载变化的显着影响。因此，在实际系统中，仅使用频谱幅度来识别电动机故障是不够的。各种负载条件和不同类型的故障都会影响频谱结构。为了识别在各种负载条件下的故障，我们必须将频带偏移和幅度视为两个主要因素。在本文中，我们使用带移和幅度技术来解决在各种负载条件和不同类型的故障下的频谱问题。我们还使用频率轴调整和特征校正的方法分别解决了频移和幅度变化问题。经过上述过程，可以获得有效的功能。然后，我们使用反向传播人工神经网络（ANN）来训练和识别故障条件。另外，我们比较了人工神经网络和传统方法之间的识别能力。本文中使用的所有理论和方法均通过不同的电机实验结果得到验证。

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