Fault diagnosis of rolling bearings using an Improved Multi-Scale Convolutional Neural Network with Feature Attention mechanism.

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.