A Novel Temporal Convolutional Network Based on Position Encoding for Remaining Useful Life Prediction

被引：0
作者：
Yang, Yinghua [1 ]
Fu, Hongxiang [1 ]
Liu, Xiaozhi [1 ]
机构：
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Peoples R China
来源：
2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC | 2023年
基金：
中国国家自然科学基金;
关键词：
Deep learning; Remaining useful life; Position encoding; Temporal convolutional network; REGRESSION;
D O I：
10.1109/CCDC58219.2023.10327490
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The technology of prognostics and health management (PHM) has developed rapidly. As one of the important tasks in PHM field, remaining useful life (RUL) prediction can effectively predict the remaining service time before machine failure, so that enterprises can make decisions in advance and avoid safety accidents. In this article, a new data-driven method is proposed, which adopts a position encoding scheme to extract more time sequence information from the original data, and then uses a novel temporal convolutional network (TCN) and attention mechanism to predict RUL. In order to evaluate the effect of the model, C-MAPSS dataset is used for testing the performance, and the results are compared with other methods, which shows that the proposed method is more effective.
引用
收藏
页码：900 / 905
页数：6
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