A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN

Abstract

Aiming at fault visualization and automatic feature extraction, this article presents a new and intelligent bearing fault diagnostic method by combining symmetrized dot pattern (SDP) representation with squeeze-and-excitation-enabled convolutional neural network (SE-CNN) model. Graphical representations of bearing states are shown intuitively by using the SDP method. Meanwhile, optimal parameters during SDP images' generation are selected to enhance the image resolution for distinctly distinguishing different bearing states and create the corresponding bearing fault sample sets. To automatically and effectively extract SDP image features, the channel attention mechanism using the SE network is integrated with the CNN network. The proposed SE-CNN-based diagnostic framework has the ability to assign certain weight to each feature extraction channel and further enforce the bearing diagnosis model focusing on the major features, meanwhile reducing the redundant information. The final diagnosis task is realized by the Softmax classifier located behind the SE-CNN model. Experimental results prove that the proposed method not only achieves the classification rate over 99% but also has better generalization ability and stability.