Rolling Bearing Fault Diagnosis Based on Meta-Learning with Few-Shot Samples
文献类型：会议论文
| 作者 | He YP(贺云鹏)2,3,4,5 ; Zang CZ(臧传治)6; Zeng P(曾鹏)2,3,4; Wang MX(王明新)1,2,3,4; Dong QW(董青卫)2,3,4,5; Liu YQ(刘玉奇)2,3,4
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| 出版日期 | 2021 |
| 会议日期 | November 8-11, 2021 |
| 会议地点 | Shenyang, China |
| 关键词 | convolutional neural network fault diagnosis few-shot learning meta-learning rolling bearing |
| 页码 | 1-6 |
| 英文摘要 | As an essential component of mechanical equipment, the state of the rolling bearing has a substantial impact on the operation of the entire automatic system. The fault diagnostic technology based on deep learning surpasses the traditional fault diagnosis technology in many aspects and dramatically improves the accuracy of fault diagnosis but requires a massive amount of labeled data for training. Generally, it takes a lot of effort to obtain tagged data in a natural industrial environment. Therefore, this paper proposes a rolling bearing fault diagnosis method based on meta-learning, which applies the experience learned in the past to new tasks to use few-shot labeled rolling bearing fault samples for training to obtain reliable diagnosis accuracy. The results show that the proposed method can significantly improve few-shot rolling bearing fault samples' accuracy than other traditional methods. |
| 产权排序 | 1 |
| 会议录 | 2021 3rd International Conference on Industrial Artificial Intelligence (IAI)
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-6654-3517-8 |
| 源URL | [http://ir.sia.cn/handle/173321/29968]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 通讯作者 | Zang CZ(臧传治) |
| 作者单位 | 1.School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China 2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China 3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 5.University of Chinese Academy of Sciences, Beijing 100049, China 6.Shenyang University of Technology, Shenyang 110870, China |
| 推荐引用方式 GB/T 7714 | He YP,Zang CZ,Zeng P,et al. Rolling Bearing Fault Diagnosis Based on Meta-Learning with Few-Shot Samples[C]. 见:. Shenyang, China. November 8-11, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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