Multi-Frequency Augmentation framework via information active capture for machinery intelligent fault diagnosis

Data-driven methods, especially deep neural network, received increasing attention in machinery fault diagnosis field. Many works focus on how to design effective model while ignoring a fundamental problem, i.e., directly using raw machinery signal as the input of model. In this work, we analyze from two aspects: model mechanism and mechanical monitoring signal, it shows the limitation of learning raw data directly, which led to the research idea of improving the generalization ability of model by multi-frequency information augmentation. In order to make machinery intelligent model capture multi-frequency information more directly and actively, Multi-Frequency Augmentation framework is proposed in this paper. Firstly, we proposed a data augmentation method to split the raw sample into sample pair. And we could choose to further augment the dataset by Frequency Components Recombination, especially under few-shot scenes. Then, Multi-Frequency Capture Network is built to achieve feature augmentation by learning the sample pair. Finally, fault diagnosis is performed on testing set. The effectiveness and compatibility of Multi-Frequency Augmentation framework is verified with two experiments, which also verifies the feasibility of the proposed research idea. In addition, it could also achieve competitive performance with latest literature methods. Further discussion indicate that the proposed framework provides a new perspective to analyze the model and dataset, which has good application potential.