基于卷积门控循环网络的滚动轴承故障诊断

针对许多基于深度学习的滚动轴承故障诊断方法在小样本数据集下诊断性能下降的问题,提出一种基于卷积门控循环神经网络的轴承故障诊断模型。该模型使用两层的卷积网络来从输入信号中提取特征,同时使用tanh函数作为激活函数,且池化层使用大池化核来进行重叠下采样。将所提取得到的高层特征连接到双向门控循环网络。合并循环网络正向和逆向的最后一个状态,并连接一层全连接层进行输出。选用凯斯西储大学的轴承故障数据集来验证模型在小样本数据集下的诊断性能,实验结果表明,相比于其他类型的模型,该模型在仅有20个训练样本的情况下依然保持97%的识别准确率。 

Faultdiagnosis of rolling bearing based on convolution gated recurrent network

In view of the phenomenon of the degraded diagnostic performance of many rolling bearing fault diagnosis methods based on deep learning under the small sample data set, a bearing fault diagnosis model based on convolution gated recurrent neural network was proposed. This model used a two-layer convolution network to extract features from the input signal, using the tanh function as the activation function, and the pooling layer used the large pooled kernel for overlapping downsampling. The extracted high-level features were connected to the bidirectional gated recurrent network. The last states of the forward and reverse directions of the recurrent network were combined, and a layer of fully connected layers was connected for output. The bearing fault data set of Case Western Reserve University was used to verify the diagnostic performance of the model under the small sample data set. The experimental results showed that the model still maintained 97% accuracy with only 20 training samples compared with other types of models.