DPCCNN: A new lightweight fault diagnosis model for small samples and high noise problem

Abstract

Obtaining large amounts of industrial data in real industrial processes is usually difficult. Meanwhile, it is difficult for deep learning-based lightweight fault diagnosis networks to obtain reliability diagnosis performance in the presence of high noise. To address these limitations, a new lightweight convolutional neural network (CNN) fault diagnosis framework, the dilated perceptually coupled convolutional neural network (DPCCNN), has been proposed for small samples and high noise problems. First, dilated layered interactive convolutional module (DLICM) is designed to obtain strong feature extraction capability, enhance the receptive field of small convolutional kernels by dilated convolution, and the self-attention mechanism compensates for the weak interactivity of deep convolution, which greatly reduces the number of parameters and computation of the model. Second, the global aggregation block (GAB) is designed to extract the contextual information of the feature map, which can focus on the basic contextual information without extensive computational requirements. The performance of this method is verified to be better than the current popular fault diagnosis models by the public dataset and the self-constructed rotor dataset, which still has good noise resistance in a lightweight framework and maintains high stability in small sample scenarios.