Intelligent Rolling Bearing Fault Diagnosis via Vision ConvNet

Feature extraction from a time sequence signal without manual information is an important part for bearing intelligent diagnosis. With the merits of signal information and feature structure information excavation, Deep ConvNet is widely used in bearing fault diagnosis and analysis under complex working conditions. However, due to the complexity of the bearing operating environment in the actual operation process, the sensitive features show different scale distribution characteristics. Meanwhile, it is known that the convolution kernel of ConvNet is usually small, which mainly focuses on the small-scale details of state distribution characteristics while ignores the identification of the overall trend of characteristic distribution. Considering that the size of convolution kernel can sense information hidden in different scales, this paper designed a one-dimensional vision ConvNet (VCN), where the architecture is composed of multilayer small kernel network and single-layer large kernel network side by side. The multi-kernel structure improves the ability of network to detect fault characteristic frequency band. By analyzing the artificially generated data and experimental data, the setting method of large convolution kernel and stride is discussed. Compared with the traditional CNN, wide first-layer kernels (WDCNN) and multiscale kernel-based ResCNN (MK-ResCNN), this network improves the recognition accuracy with a better stable training process for rolling bearing fault classification.

Automated summary

This paper designed a one-dimensional vision ConvNet (VCN) which improves the network's ability to detect fault characteristic frequency bands by using a multi-kernel structure. By analyzing artificially generated data and experimental data, the paper discusses the setting method of large convolution kernels and strides. Compared to traditional CNN, wide first-layer kernels (WDCNN) and multiscale kernel-based ResCNN (MK-ResCNN), this network enhances recognition accuracy through a more stable training process for rolling bearing fault classification.