Deep Learning Approaches to Remaining Useful Life Prediction: A Survey

被引：3

作者：

Cummins, Logan ^{[1
]}

Killen, Brad ^{[1
]}

Thomas, Kirby ^{[1
]}

Barrett, Paul ^{[1
]}

Rahimi, Shahram ^{[1
]}

Seale, Maria ^{[2
]}

机构：

[1] Mississippi State Univ, Dept Comp Sci & Engn, Starkville, MS 39762 USA

[2] US Army, Engn Res & Dev Ctr, Vicksburg, MS USA

来源：

2021 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2021) | 2021年

关键词：

deep learning; remaining useful life; prognostics; SHORT-TERM-MEMORY; ALGORITHM; NETWORK;

D O I：

10.1109/SSCI50451.2021.9659965

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Prognostic and Health Management (PHM) systems have multiple facets one would need to perfect for an efficient system. One of these is the prediction of remaining useful life (RUL), which is the task of producing a number of time units (cycles, minutes, days, etc) until a part of the system or the system as a whole will fail. Over the years, deep learning approaches have been used to effectively perform this task, and these approaches fall into multiple different types of deep learning architectures. While non deep learning approaches exist, this paper focuses on a number of different deep learning approaches to solving the problem of RUL prediction.

引用

页数：9

共 48 条

[41] An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation [J].

Xia, Tangbin ;

Song, Ya ;

Zheng, Yu ;

Pan, Ershun ;

Xi, Lifeng .

COMPUTERS IN INDUSTRY, 2020, 115

[42]

Yuan M, 2016, 2016 IEEE/CSAA INTERNATIONAL CONFERENCE ON AIRCRAFT UTILITY SYSTEMS (AUS), P135, DOI 10.1109/AUS.2016.7748035

[43] A new bearing fault diagnosis method based on modified convolutional neural networks [J].

Zhang, Jiangquan ;

Sun, Yi ;

Guo, Liang ;

Gao, Hongli ;

Hong, Xin ;

Song, Hongliang .

CHINESE JOURNAL OF AERONAUTICS, 2020, 33 (02) :439-447

[44] Deep Fuzzy Echo State Networks for Machinery Fault Diagnosis [J].

Zhang, Shaohui ;

Sun, Zhenzhong ;

Wang, Man ;

Long, Jianyu ;

Bai, Yun ;

Li, Chuan .

IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2020, 28 (07) :1205-1218

[45] Remaining Useful Life Estimation Using Long Short-Term Memory Neural Networks and Deep Fusion [J].

Zhang, Yang ;

Hutchinson, Paul ;

Lieven, Nicholas A. J. ;

Nunez-Yanez, Jose .

IEEE ACCESS, 2020, 8 :19033-19045

[46] Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries [J].

Zhang, Yongzhi ;

Xiong, Rui ;

He, Hongwen ;

Pecht, Michael G. .

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2018, 67 (07) :5695-5705

[47]

Zhao Z., 2020, SSRN

[48]

Zheng S, 2017, 2017 IEEE INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (ICPHM), P88, DOI 10.1109/ICPHM.2017.7998311

← 1 2 3 4 5 →