Knowledge Commons of Institute of Automation,CAS
Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction | |
Ruibing Jin; Min Wu; Keyu Wu; Kaizhou Gao; Zhenghua Chen; Xiaoli Li | |
发表期刊 | IEEE/CAA Journal of Automatica Sinica
![]() |
ISSN | 2329-9266 |
2022 | |
卷号 | 9期号:8页码:1427-1439 |
摘要 | Accurate remaining useful life (RUL) prediction is important in industrial systems. It prevents machines from working under failure conditions, and ensures that the industrial system works reliably and efficiently. Recently, many deep learning based methods have been proposed to predict RUL. Among these methods, recurrent neural network (RNN) based approaches show a strong capability of capturing sequential information. This allows RNN based methods to perform better than convolutional neural network (CNN) based approaches on the RUL prediction task. In this paper, we question this common paradigm and argue that existing CNN based approaches are not designed according to the classic principles of CNN, which reduces their performances. Additionally, the capacity of capturing sequential information is highly affected by the receptive field of CNN, which is neglected by existing CNN based methods. To solve these problems, we propose a series of new CNNs, which show competitive results to RNN based methods. Compared with RNN, CNN processes the input signals in parallel so that the temporal sequence is not easily determined. To alleviate this issue, a position encoding scheme is developed to enhance the sequential information encoded by a CNN. Hence, our proposed position encoding based CNN called PE-Net is further improved and even performs better than RNN based methods. Extensive experiments are conducted on the C-MAPSS dataset, where our PE-Net shows state-of-the-art performance. |
关键词 | Convolutional neural network (CNN) deep learning position encoding remaining useful life prediction |
DOI | 10.1109/JAS.2022.105746 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49651 |
专题 | 学术期刊_IEEE/CAA Journal of Automatica Sinica |
推荐引用方式 GB/T 7714 | Ruibing Jin,Min Wu,Keyu Wu,et al. Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(8):1427-1439. |
APA | Ruibing Jin,Min Wu,Keyu Wu,Kaizhou Gao,Zhenghua Chen,&Xiaoli Li.(2022).Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction.IEEE/CAA Journal of Automatica Sinica,9(8),1427-1439. |
MLA | Ruibing Jin,et al."Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction".IEEE/CAA Journal of Automatica Sinica 9.8(2022):1427-1439. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
JAS-2022-0561.pdf(4588KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论