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Fault feature enhancement and diagnosis method of rolling bearing based on boundary-assisted discrimination

Published:2024-04-24 author:LI Bailin, LU Dachen, FU Wenlong, et al. Browse: 993 Check PDF documents

Fault feature enhancement and diagnosis method of rolling bearing

based on boundary-assisted discrimination

LI Bailin, LU Dachen, FU Wenlong, CHEN Yupeng

(College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

Abstract: As the vital component of mechanical equipment, rolling bearings play a critical role in ensuring the safety and stability of equipment.To solve the problem of data imbalance in rolling bearing fault diagnosis, a boundary-assisted discriminative auxiliary classifier generative adversarial network (BD-ACGAN) model was proposed. Firstly, a boundary-assisted discriminator was designed to extract the boundary feature details of faulty samples, to guide the generator to generate more realistic samples for addressing the issue of data imbalance.Secondly, an adaptive weight loss module was employed to dynamically adjust the loss weights, enabling the model to focus more on important feature information and improve the quality of sample generation and feature representation.Further, generated samples and real samples were employed for enhancement training of BD-ACGAN model, to improve the generalization ability and diagnostic capability. Finally, the feature enhancement capability and diagnostic effectiveness of BD-ACGAN model were verified through ablation experiments and comparison experiments. The rolling bearing data sets from Case Western Reserve University and Xi'an Jiaotong University were used to verify the model. The experimental results show that BD-ACGAN model can effectively use the boundary characteristics of fault samples to solve the problem of data imbalance, and the fault diagnosis accuracy is 98.79%, which is better than other control models, and provides a new method for the fault diagnosis of rolling bearings.

Key words: bearing fault diagnosis; data imbalance; boundary-assisted discriminative auxiliary classifier generative adversarial network(BD-ACGAN); fault feature enhancement; adaptive weight loss; dataset augmentation

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