A novel edge intelligence application model with lightweight network and antinoise ability for bearing fault diagnosis

Abstract

With the rapid development of deep learning, edge intelligence applications (EIA) have achieved numerous results. However, redundant parameters of model and strong noise pollution pose challenges to EIA for bearing fault diagnosis. To solve these challenges, a model with lightweight network and antinoise ability was proposed for bearing fault diagnosis. First, a novel pluggable channel slimming module was designed to make the model lightweight, which can effectively reduce the parameters and computation of the model. Second, an antinoise learning network is proposed, which has a noise discriminator to enhance the network’s feature extraction capability through supervised learning. Finally, an adaptive input module was proposed to enhance the generalization ability of the model, which can adaptively adjust the input information under different application environments to improve the stability and accuracy of the model. The performance of the proposed model was verified through the test rig experiments on two types of train axle box bearings datasets, which indicated the proposed model achieves more than 89% diagnostic accuracy at −10 dB.