Rotating machinery fault diagnosis based on one-dimensional convolutional neural network and modified multi-scale graph convolutional network under limited labeled data

Current rotating machinery fault diagnosis methods are primarily based on deep learning methods, which necessitate a large amount of data for training to achieve better results. However, it is quite challenging to collect and mark fault data in industry, limiting the use of mainstream methods. In this paper, a new framework for rotating machinery fault diagnosis is proposed that combines one-dimensional convolutional neural network and modified multi-scale graph convolutional network. Firstly, the category information of samples and the correlation between samples are used to build the graph-structured data. Secondly, by cascading standard graph convolutional layers, a modified multi-scale graph convolutional network is proposed, which makes the model better at extracting features at different scales. Finally, the one-dimensional convolutional neural network and the modified multi-scale graph convolutional network are combined, and an optimized training strategy is introduced to address the problem of model training overfitting with limited labeled data. The proposed method is shown to be superior for rotating machinery fault diagnosis with limited labeled data by applying the proposed framework to different datasets and comparing it to various diagnostic models.