关键词: 卷积神经网络, Teager 能量算子, 声学故障诊断, 滚动轴承, 跨转速域工况, 全局平均池化

Abstract:

In view of the difficulty to collect bearing vibration signals in complex machinery and the poor accuracy of traditional fault diagnosis methods under cross working speed conditions, a rolling bearing acoustic fault diagnosis method is proposed based on TEO-CNN. Teager energy operator of raw acoustic signals is taken as the input of TEO-CNN model, the CNN is employed to extract the abstract features from inputs, and the global average pooling layer and the fully connected layer are combined to recognize the bearing health status. TEO-CNN is verified on bearing acoustic experimental data sets, and cross working speed conditions are simulated by constructing different bearing acoustic data sets. Experimental results show that compared with traditional convolutional neural networks and machine learning models, the proposed TEO-CNN shows obvious superiority, and the prediction accuracy is always higher than 95% under cross working speed conditions.

Key words: convolutional neural network, Teager energy operator, acoustic-based fault diagnosis, rolling bearing, cross working speed conditions, global average pooling