A novel intelligent bearing fault diagnosis method based on image enhancement and improved convolutional neural network

Bearing fault diagnostic is crucial for ensuring the normal operation of equipment. However, the working environment of mechanical equipment is often very harsh, the collection of fault data is limited in the actual industrial process. The problem of insufficient fault samples occurs, which makes the feature extraction ability of the deep learning models drop dramatically. To solve this terrible problem, an intelligent diagnosis framework based on image enhancement and an improved convolutional neural network (IEICNN) is outlined in this article. First, a signal conversion method based on feature fusion is employed to convert multi-channel one-dimensional vibration signals into red - green - blue (RGB) images to obtain more comprehensive fault data representation. In addition, a new image enhancement method is proposed, which uses the improved Squeeze and ExcitationResidual Network (SE-ResNet) to learn and extract the advanced feature map, and uses deconvolution neural networks to reconstruct RGB image. Finally, the Softmax classifier is applied to identify the health conditions of the bearing. The superiority and robustness of IEICNN are validated using two bearing datasets. By comparing with several progressive comparison approaches, it is proven that the proposed approach has better robustness and effectiveness in the case of limited data scarcity.