摘要详情
58 / 2021-07-12 15:02:59
Fault diagnosis of rolling bearing based on CNN with attention mechanism and dynamic learning rate
终稿
It is of great importance to diagnose early failures of rolling bearings accurately and timely for ensuring the safe and reliable operation of equipment. However, early faults have the characteristics of weak feature information and low signal-to-noise ratio, which increases the difficulty of fault identification. Therefore, based on deep learning theory, this paper proposes a convolutional neural network (CNN) model based on attention mechanism and dynamic learning rate for intelligent diagnosis and recognition of early faults of rolling bearings. The model introduces the attention mechanism to improve the learning performance of the convolutional network for effective information, so that the model can learn the discriminative and important features. A dynamic adjustment strategy of learning rate is designed to improve the convergence speed and performance of the model. Finally, the proposed method is analyzed using XJTU-SY rolling bearing dataset. Experimental results indicate that compared with other fault intelligent diagnosis methods, the proposed method can effectively identify the early fault of rolling bearings under different operating conditions, and has higher diagnostic accuracy.
重要日期
-
会议日期
10月21日
2021
至10月23日
2021
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10月26日 2021
注册截止日期
主办单位
Southeast University, China
联系方式
历届会议
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2023年11月02日 中国 China
International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence -
2022年11月30日 中国 Harbin
International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence -
2020年10月15日 中国 Xi'an
International Conference on Sensing, Measurement and Data Analytics in the era of Artificial Intelligence
