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Fault diagnosis of rolling bearings using an Improved Multi-Scale Convolutional Neural Network with Feature Attention
Zifei Xu1, Chun Li2, Yang Yang1
1School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, PR China; Department of Maritime and Mechanical Engineering, Liverpool John Moores University, Liverpool, Byrom Street, L3 3AF, UK.
Abstract:
Machine learning techniques have been successfully applied for the intelligent fault diagnosis of rolling bearings in recent years. This study has developed an Improved Multi-Scale Convolutional Neural Network integrated with a Feature Attention mechanism (IMS-FACNN) model to address the poor performance of traditional CNN-based models under unsteady and complex working environments. The proposed IMS-FACNN has a good extrapolation performance because of the novel IMS coarse grained procedure with training interference and the introduced the feature attention mechanism, which improves the model's generalization ability. The proposed IMS-FACNN model has a better performance than existing methods in all the examined scenarios including diagnosing the bearing fault of a real wind turbine. The results show that the reliability and superiority of the IMS-FACNN model in diagnosing faults of rolling bearings.