State Key Laboratory for Manufacturing Systems Engineering, Xi'an JiaoTong University, Xi'an, China.
PLoS One. 2020 Feb 4;15(2):e0228324. doi: 10.1371/journal.pone.0228324. eCollection 2020.
To solve the problem of low accuracy in traditional fault diagnosis methods, a novel method of combining generalized frequency response function(GFRF) and convolutional neural network(CNN) is proposed. In order to accurately characterize system state information, this paper proposed a variable step size least mean square (VSSLMS) adaptive algorithm to calculate the second-order GFRF spectrum values under normal and fault states; In order to improve the ability of fault feature extraction, a convolution neural network (CNN) with gradient descent learning rate and alternate convolution layer and pooling layer is designed to extract the fault features from GFRF spectrum. In the proposed method, the second-order GFRF spectrum of each state of Permanent Magnet Synchronous Motor (PMSM) is obtained by VSSLMS; Then, the two-dimension GFRF spectrum, which is regarded as the gray value of the image,will be further transformed into image. Finally, the CNN is trained with learning rate by gradient descent way to realize the fault diagnosis of PMSM. Experimental results indicate that the accuracy of proposed method is 98.75%, which verifies the reliability of the proposed method in application of PMSM fault diagnosis.
为了解决传统故障诊断方法精度低的问题，提出了一种将广义频率响应函数（GFRF）和卷积神经网络（CNN）相结合的新方法。为了准确地描述系统状态信息，本文提出了一种变步长最小均方（VSSLMS）自适应算法，以计算正常和故障状态下的二阶 GFRF 频谱值；为了提高故障特征提取能力，设计了具有梯度下降学习率和交替卷积层和池化层的卷积神经网络（CNN），以从 GFRF 频谱中提取故障特征。在所提出的方法中，通过 VSSLMS 获得永磁同步电机（PMSM）每个状态的二阶 GFRF 频谱；然后，将二维 GFRF 频谱（视为图像的灰度值）进一步转换为图像。最后，通过梯度下降方法用学习率对 CNN 进行训练，实现 PMSM 的故障诊断。实验结果表明，所提方法的准确率为 98.75%，验证了所提方法在 PMSM 故障诊断中的可靠性。
