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

[11]

Goodfellow I., 2016, DEEP LEARNING, V1

[12] Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition [J].

Habbouche, Houssem ;

Benkedjouh, Tarak ;

Zerhouni, Noureddine .

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2021, 114 (1-2) :145-157

[13] Early and robust remaining useful life prediction of supercapacitors using BOHB optimized Deep Belief Network [J].

Haris, Muhammad ;

Hasan, Muhammad Noman ;

Qin, Shiyin .

APPLIED ENERGY, 2021, 286

[14] Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network [J].

Hinchi, Ahmed Zakariae ;

Tkiouat, Mohamed .

PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING IN DATA SCIENCES (ICDS2017), 2018, 127 :123-132

[15] A fast learning algorithm for deep belief nets [J].

Hinton, Geoffrey E. ;

Osindero, Simon ;

Teh, Yee-Whye .

NEURAL COMPUTATION, 2006, 18 (07) :1527-1554

[16]

Hochreiter S., 1997, Neural Computation, V9, P1735

[17] Remaining Useful Life Prognosis for Turbofan Engine Using Explainable Deep Neural Networks with Dimensionality Reduction [J].

Hong, Chang Woo ;

Lee, Changmin ;

Lee, Kwangsuk ;

Ko, Min-Seung ;

Kim, Dae Eun ;

Hur, Kyeon .

SENSORS, 2020, 20 (22) :1-19

[18] Remaining Useful Life Estimation Using Deep Convolutional Generative Adversarial Networks Based on an Autoencoder Scheme [J].

Hou, Guisheng ;

Xu, Shuo ;

Zhou, Nan ;

Yang, Lei ;

Fu, Quanhao .

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2020, 2020

[19] A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions [J].

Huang, Cheng-Geng ;

Huang, Hong-Zhong ;

Li, Yan-Feng .

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2019, 66 (11) :8792-8802

[20]

Jaeger H., 2001, GMD-Rep. 148

← 1 2 3 4 5 →