基于卷积神经网络和小波包的微型振动马达的故障检测

Fault Detection for Miniature Vibration Motor Based on Wavelet and Convolutional Neural Network

针对手机振动马达检测量大、检测困难等问题,引入卷积神经网络对故障马达波形图进行分类检测。用采集卡采集马达转动时的原始电压信号,对电压信号进行两层小波包分解并重构低频信号,截取原始信号减去重构信号的波形图片进行预处理作为数据集。再用TensorFlow框架训练数据模型,对振动马达电刷不良、波形异常、波形跌落、磁场不良、良品5种类型进行分类,用改进的卷积网络模型测试集准确率达到了98.76%。因此基于改进的卷积神经网络有更好的诊断效果,且对提高故障诊断准确率有一定的作用。

In order to detect the vibration motor of mobile phone,neural network was introduced to detect the waveform of the motor.LabVIEW was used to collect the original signal when the motor was rotating,the original signal was decomposed by using two-layer wavelet packet,the decomposed low-frequency signal was reconstructed,the original signal minus the reconstructed signal image was intercepted for preprocessing,and the preprocessed image was served as the data set.The model was trained by Tensor Flow.The five types of bad brush,abnormal waveform,waveform drop,bad magnetic field and good product were classified,and the accuracy rate of the improved convolutional network model had reached 98.76%.The results show that the improved neural network has better diagnosis effect and can improve the accuracy of fault diagnosis.