关键词: 注意力增强卷积, 深度卷积神经网络, 特征融合, 航空发动机滚动轴承, 故障诊断

Abstract: The existing fault diagnosis methods based on deep convolutional neural networks have only considered the extraction of local features while ignoring global information. To address this problem, an attention mechanism that can grasp global information is integrated into the convolution layer. The purpose is to allow both attention mechanism parameters and convolutional layer parameters to participate in network training. An attention augmented convolutional neural network for mechanical fault diagnosis is proposed. First, the high-dimensional feature modulus of the vibration signal of a rolling bearing fault is extracted using empirical mode decomposition, variational mode decomposition, and wavelet packet decomposition. Then, multi-channel samples composed of the feature modulus are input into the attention augmented convolutional neural network for training. The feature modulus are adaptively fused and selected by the network to mine the hidden information. Finally, the classifier Softmax is used to recognize and classify the results. The fault diagnosis of rolling bearings at high rolling speeds is carried out by the trained model. The proposed model is further tested by signals with different signal-to-noise ratios to verify the generalization ability of the network and the effectiveness of fault diagnosis. The experimental results show that the proposed method can accurately and effectively classify the damage degree of different bearing faults in aero-engines.

Key words: attentionaugmentedconvolution, deepconvolutionalneuralnetwork, featurefusion, aero-enginerollingbearing, faultdiagnosis