国外文献 > 1.轴承 > 7. 智能方法与计算 > 机器学习
关键词搜索：
| 标 题： |
A Comparison of Deep Transfer Learning Methods on Bearing Fault Detection
深度迁移学习方法在轴承故障检测中的比较 |
| 作 者： |
Bilgin Umut Deveci
Ozlem Albayrak
Mert Celtikoglu
Perin Unal
Tilbe Alp
Pinar Kirci |
| 关键词： |
Transfer learning [迁移学习]
GoogLeNet [ GoogLeNet]
CNN [ CNN]
Bearing fault diagnostics [ 轴承故障诊断]
CWRU bearing dataset [ CWRU轴承数据集]
Deep learning [ 深度学习]
AlexNet [ AlexNet]
ResNet-50 [ ResNet-50] |
| 出版年： |
2021 |
| 会议名： |
8th International Conference on Future Internet of Things and Cloud (FiCloud 2021)
第八届未来物联网与云（FiCloud 2021）国际会议 |
| 届 次： |
8th |
| 摘 要： |
In rotating machinery, bearings are widely used as universal components. Bearings are placed in critical positions, therefore, in predictive maintenance, it is crucial to diagnose bearing faults accurately and in a timely manner. In this paper, three diverse pre-trained networks on bearing fault diagnosis are discussed. A generic intelligent bearing fault diagnosis system based on AlexNet, GoogLeNet and ResNet-50 with transfer learning is proposed to distinguish and classify different bearing faults. Three bearing faults at all various loads and speeds selected from the Case Western Reserve University (CWRU) bearing dataset were converted to time-frequency images, in order to improve the performance of the proposed networks. Results showed that when compared to previous methods, the proposed method achieved outstanding execution, with overall classification training accuracy of 100%, validation accuracy of 99.27%.
在旋转机械中，轴承被广泛用作通用部件。由于轴承位于关键位置，因此在预测性维护中，准确且及时地诊断轴承故障至关重要。本文讨论了三种用于轴承故障诊断的预训练网络。提出了一种基于AlexNet、GoogLeNet和ResNet-50的通用智能轴承故障诊断系统，并结合迁移学习技术，用于识别和分类不同类型的轴承故障。从Case Western Reserve大学（CWRU）轴承数据集选取了三种不同负载和转速下的轴承故障，并将其转换为时频图像，以提升所提网络的性能。结果表明，与以往方法相比，该方法表现优异，整体分类训练准确率达到100%，验证准确率为99.27%。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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