CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery

Abstract

Sensor techniques and emerging CNN models have greatly facilitated the development of collaborative fault diagnosis. Existing CNN models apply different fusion schemes to achieve reliable fault identification based on multisensor data. Few CNN models, however, take into account both the intrinsic correlations and the distribution gap between different signals, which may result in a limited exploration of multisource data. To address this issue, a novel convolutional fusion framework called a collaborative fusion convolutional neural network (CFCNN) is developed in this paper. More specifically, a multiscale shrinkage denoising module (MSDM) is developed first to extract multilevel modality-specific features from different mechanical signals. Then, drawing inspiration from the intermediate fusion scheme, a central fusion module (CFM) is introduced to explore the intrinsic correlations and integrate cross-modal features. Moreover, an online label smoothing training (OLST) strategy is applied to reduce overfitting and promote better classification performance of CFCNN. The developed CFCNN is expected to shed new light on collaborative fault diagnosis using the intermediate fusion scheme. The efficacy of the developed CFCNN is verified through the cylindrical rolling bearing dataset and the planetary gearbox dataset.