%0 Journal Article
%T 基于卷积神经网络的异步电机故障诊断<br>Motor Fault Diagnosis Based on Convolutional Neural Networks
%A 王丽华
%A 谢阳阳
%A 周子贤
%A 张永宏
%A 赵晓平
%J 振动.测试与诊断
%D 2017
%R 10.16450/j.cnki.issn.10046801.2017.06.021
%X 由于电机内部结构的复杂性，使得其故障特征与故障类型之间存在较强的非线性关系；目前用于异步电机故障诊断的方法都是人工手动提取特征，这需要大量的先验知识、丰富的信号处理理论和实际经验作为支撑，诊断效率不高；同时用于模式识别时的样本量过少，会导致网络过拟合等问题。针对以上问题，提出了基于短时傅里叶变换(short time fourier transform，简称STFT)和卷积神经网络(convolutional neural networks, 简称CNN)的电机故障诊断方法。该方法以单一振动信号为监测信号，使用STFT将故障信号转换成时频谱图，构建大量不同故障样本，以确保样本多样性，提高网络鲁棒性。将预处理后的样本作为CNN的输入，有监督地调整网络参数，以实现准确的电机故障诊断。将所提出的STFT+CNN算法分别与传统的电机故障诊断方法及堆叠降噪自编码进行比较分析。试验结果表明，该方法能够更有效地进行电机故障诊断。<br>Due to the complexity of the internal structure of the motor, there is a strong nonlinear relationship between the fault feature and the type of fault; also, the methods of asynchronous motor fault diagnosis are manual extraction of features, which requires a large number of prior knowledge, abundant signal processing theory and practical experience as support; at the same time, the amount of samples used in pattern recognition is too small, which may lead to overfitting. Therefore, a fault diagnosis method based on short-time Fourier transform (STFT) and convolutional, neural, networks (CNN) is proposed. The method uses a single vibration signal as a monitoring signal, and uses STFT to convert the signal into a time spectrum, and serves as a sample input for the network to supervise the training, which ensures the diversity of the sample, improves the robustness of the network and achieves accurate fault diagnosis. It is compared with the traditional motor fault diagnosis method and the stacked denoising autoencoder. The test results show that this method can effectively diagnose motor fault.
%K 电机
%K 振动信号
%K 短时傅里叶变换
%K 卷积神经网络&lt
%K br&gt
%K motor
%K vibration signal
%K short-time Fourier transform
%K convolutional neural networks
%U http://zdcs.nuaa.edu.cn/ch/reader/view_abstract.aspx?file_no=201706021&flag=1