Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction

doi:10.1109/JAS.2022.105746

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	Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction
	Ruibing Jin; Min Wu; Keyu Wu; Kaizhou Gao; Zhenghua Chen; Xiaoli Li
刊名	IEEE/CAA Journal of Automatica Sinica
	2022
卷号	9 期号:8 页码:1427-1439
关键词	Convolutional neural network (CNN) deep learning position encoding remaining useful life prediction
ISSN号	2329-9266
DOI	10.1109/JAS.2022.105746
英文摘要	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.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49651]
专题	自动化研究所_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.