ArticleSensors (Basel, Switzerland)2023
A Deep Learning Method for Rolling Bearing Fault Diagnosis Based on Attention Mechanism and Graham Angle Field.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- ITDFNet: a multi-dimensional fusion image and temporal dual-branch network for bearing fault diagnosis.Scientific reports · 2026Article
- Enhancing unsupervised bearing fault diagnosis through structured prediction in latent subspace.Scientific reports · 2025Article
- Research on Fault Diagnosis of Rolling Bearing Based on Gramian Angular Field and Lightweight Model.Sensors (Basel, Switzerland) · 2024Article
- Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
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Abstract
Focusing on the low accuracy and timeliness of traditional fault diagnosis methods for rolling bearings which combine massive amounts of data, a fault diagnosis method for rolling bearings based on Gramian angular field (GAF) coding technology and an improved ResNet50 model is proposed. Using the Graham angle field technology to recode the one-dimensional vibration signal into a two-dimensional feature image, using the two-dimensional feature image as the input for the model, combined with the advantages of the ResNet algorithm in image feature extraction and classification recognition, we realized automatic feature extraction and fault diagnosis, and, finally, achieved the classification of different fault types. In order to verify the effectiveness of the method, the rolling bearing data of Casey Reserve University are selected for verification, and compared with other commonly used intelligent algorithms, the results show that the proposed method has a higher classification accuracy and better timeliness than other intelligent algorithms.
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