An Attention EfficientNet-Based Strategy for Bearing Fault Diagnosis under Strong Noise

With the continuous development of artificial intelligence, data-driven fault diagnosis methods are gradually attracting widespread attention. However, in practical industrial applications, noise in the working environment is inevitable. This leads to the fact that the performance of traditional intelligent diagnosis methods is hardly sufficient to satisfy the requirements. In this paper, a developed intelligent diagnosis framework is proposed to overcome this deficiency. The main contributions of this paper are as follows: Firstly, a fault diagnosis model is established using EfficientNet, which achieves optimal diagnosis performance with limited computing resources. Secondly, an attention mechanism is introduced into the basic model for accurately establishing the relationship between fault features and fault modes, while improving the diagnosis accuracy in complex noise environments. Finally, to explain the proposed method, the weights and features of the model are visualized, and further attempts are made to analyze the reasons for the high performance of the model. The comprehensive experiment results reveal the superiority of the proposed method in terms of accuracy and stability in comparison with other benchmark diagnosis approaches. The diagnostic accuracy under actual working conditions is 86.24%.