基于SincNet网络结合注意力机制齿轮箱故障诊断

针对传统卷积神经网络(CNN)在齿轮箱中故障诊断准确率不高、特征提取方面表现欠佳的问题,提出了SincNet网络结合注意力机制齿轮箱故障诊断方法。首先,采用参数化的Sinc函数设计滤波器作为卷积层来代替传统CNN的第1个卷积层,得到SincNet网络结构,提取输入数据的特征信息;其次,结合具有Softmax的注意力机制(Att)增强特征信息。最后,采用齿轮箱故障数据集对所提出的方法进行实验验证,结果表明,所提方法平均诊断准确率达到99.68%,均高于对比方法。此外,通过特征图可视化分析,该方法能够准确定位输入数据中的识别信息,能更好地理解神经网络的特征提取过程,为机械振动信号的特征提取过程提供了参考。

Fault Diagnosis of Gearbox Based on SincNet and Attention Mechanism

Aiming at the problem that the traditional convolutional neural network has low accuracy and poor performance in feature extraction in gearbox fault diagnosis, a SincNet combined with attention mechanism method for gearbox fault diagnosis was proposed. First, use the parameterized Sinc function to design the filter and obtain the Sinc convolutional layer, Sinc convolutional layer replace the first convolutional layer of traditional CNN to construct the SincNet network structure, Extract the characteristic information of the input data. Then, combined attention Mechanism with Softmax enhances characteristic information. Finally, the gearbox fault data set was used to verify the proposed method. The results show that the average diagnostic accuracy of the proposed method is 99.68%, which is higher than that of the comparison method. In addition, the method can accurately locate the recognition information in the input data and better understand the feature extraction process of neural network through the visual analysis of the feature map, which provides a reference for the feature extraction process of mechanical vibration signals.