Temporal convolutional attention network for remaining useful life estimation

被引：0
|
作者：
Liu L. [1 ,2 ]
Pei X. [1 ,2 ]
Lei X. [3 ]
机构：
[1] School of Automation and Engineering, University of Science and Technology Beijing, Beijing
[2] Shunde Graduate School, University of Science and Technology Beijing, Foshan
[3] Office of Information Construction and Management, University of Science and Technology Beijing, Beijing
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 08期
基金：
中国国家自然科学基金;
关键词：
attention mechanism; deep learning; remaining useful life; temporal convolutional network;
D O I：
10.13196/j.cims.2022.08.009
中图分类号：
学科分类号：
摘要：
Remaining useful life (RUL) prediction is of great significance for ensuring the safe operation of modern industrial equipment and reducing maintenance costs. At present, the existing RUL models based on recurrent neural networks are complex in structure and lack an effective mechanism to extract important degradation information from multi-sensor data. The new Temporal Convolutional Attention Network (TCAN) model was proposed for RUL estimation. A Temporal Convolutional Neural (TCN) network with a simple structure was used in TCAN to extract the degradation features from the sensor data, and then the attention mechanism was used to extract the important degradation information. The learned high-level feature representation was flattened and fed into a fully connected layer to output the predicted RUL. Compared with other methods on the C-MAPSS dataset, the experimental results showed that the TCAN could more effectively improve the accuracy of remaining life prediction. © 2022 CIMS. All rights reserved.
引用
收藏
页码：2375 / 2386
页数：11
相关论文
共 29 条
  • [1] KORDESTANI M, SAIF M, ORCHARD M E, Et al., Failure prognosis and applications A survey of recent literature, IEEE Transactions on Reliability, 70, 2, pp. 728-748, (2019)
  • [2] CHEN C, ZHANG B, VACHTSEVANOS G., Prediction of machine health condition using neuro-fuzzy and Bayesian algorithms, IEEE Transactions on Instrumentation and Measurement, 61, 2, pp. 297-306, (2011)
  • [3] SOUALHI A, MEDJAHER K, ZERHOUNI N., Bearing-health monitoring based on Hilbert-Huang transform, support vector machine, and regression, IEEE Transactions on Instrumentation and Measurement, 64, 1, pp. 52-62, (2014)
  • [4] GAO H, LIANG L, CHEN X, Et al., Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization [J], Chinese Journal of Mechanical Engineering, 2 8, 1, pp. 96-105, (2015)
  • [5] SIKORSKA J Z, HODKIEWICZ M, MA L., Prognostic modelling options for remaining useful life estimation by industry, Mechanical Systems and Signal Processing, 25, 5, pp. 1803-1836, (2011)
  • [6] KHELIF R, CHEBEL-MORELLO B, MALINOWSKI S, Et al., Direct remaining useful life estimation based on support vector regression, IEEE Transactions on Industrial Electronics, 64, 3, pp. 2276-2285, (2016)
  • [7] LIU Hui, LIU Zhenyu, JIAWeiqiang, Et al., Current research and challenges of deep learning for equipment remaining useful life prediction, Computer Integrated Manufacturing Systems, 27, 1, pp. 34-52, (2021)
  • [8] MA J, SU H, ZHAO W, Et al., Predicting the remaining useful life of an aircraft engine using a stacked sparse autoencoder with multilayer self-learning [J], Complexity, 2018, pp. 1-13, (2018)
  • [9] SONG Ya, XIA Tangbin, ZHENG Yu, Et al., Remaining useful life prediction of turbofan engine based on Autoencoder-B L S T M [J], Computer Integrated Manufacturing Systems, 25, 7, pp. 1611-1619, (2019)
  • [10] REN L, SUN Y, GUI J, Et al., Bearing remaining useful life prediction based on deep autoencoder and deep neural networks, Journal of Manufacturing Systems, 48, pp. 71-77, (2018)
123