A time window neural network based framework for Remaining Useful Life estimation

Pin Lim; Chi Keong Goh; Kay Chen Tan

doi:10.1109/IJCNN.2016.7727410

A time window neural network based framework for Remaining Useful Life estimation

Pin Lim
, Chi Keong Goh
, Kay Chen Tan

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

66 Citations (Scopus)

Abstract

This paper develops a framework for determining the Remaining Useful Life (RUL) of aero-engines. The framework includes the following modular components: creating a moving time window, a suitable feature extraction method and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on the publicly available C-MAPSS dataset. The prognostic accuracy of the proposed algorithm is also compared against other state-of-the-art methods available in the literature and it has been shown that the proposed framework has the best overall performance.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Publisher	IEEE
Pages	1746-1753
Volume	2016-October
ISBN (Print)	9781509006199
DOIs	https://doi.org/10.1109/IJCNN.2016.7727410
Publication status	Published - 31 Oct 2016
Externally published	Yes
Event	2016 International Joint Conference on Neural Networks (IJCNN 2016) - Vancouver Convention Centre , Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016 http://www.wcci2016.org/

Publication series

Name
Volume	2016-October

Conference

Conference	2016 International Joint Conference on Neural Networks (IJCNN 2016)
Abbreviated title	IJCNN 2016
Place	Canada
City	Vancouver
Period	24/07/16 → 29/07/16
Internet address	http://www.wcci2016.org/

Research Keywords

C-MAPSS
Feature Extraction
Moving Time Window
Neural Networks (NN)
Prognostics
RUL Estimation

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10.1109/IJCNN.2016.7727410

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@inproceedings{b8d44f4772f64b228086e773f58d1c37,

title = "A time window neural network based framework for Remaining Useful Life estimation",

abstract = "This paper develops a framework for determining the Remaining Useful Life (RUL) of aero-engines. The framework includes the following modular components: creating a moving time window, a suitable feature extraction method and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on the publicly available C-MAPSS dataset. The prognostic accuracy of the proposed algorithm is also compared against other state-of-the-art methods available in the literature and it has been shown that the proposed framework has the best overall performance.",

keywords = "C-MAPSS, Feature Extraction, Moving Time Window, Neural Networks (NN), Prognostics, RUL Estimation",

author = "Pin Lim and Goh, \{Chi Keong\} and Tan, \{Kay Chen\}",

year = "2016",

month = oct,

day = "31",

doi = "10.1109/IJCNN.2016.7727410",

language = "English",

isbn = "9781509006199",

volume = "2016-October",

publisher = "IEEE",

pages = "1746--1753",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

address = "United States",

note = "2016 International Joint Conference on Neural Networks (IJCNN 2016), IJCNN 2016 ; Conference date: 24-07-2016 Through 29-07-2016",

url = "http://www.wcci2016.org/",

}

Lim, P, Goh, CK & Tan, KC 2016, A time window neural network based framework for Remaining Useful Life estimation. in Proceedings of the International Joint Conference on Neural Networks. vol. 2016-October, 7727410, IEEE, pp. 1746-1753, 2016 International Joint Conference on Neural Networks (IJCNN 2016), Vancouver, Canada, 24/07/16. https://doi.org/10.1109/IJCNN.2016.7727410

A time window neural network based framework for Remaining Useful Life estimation. / Lim, Pin; Goh, Chi Keong; Tan, Kay Chen.
Proceedings of the International Joint Conference on Neural Networks. Vol. 2016-October IEEE, 2016. p. 1746-1753 7727410.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A time window neural network based framework for Remaining Useful Life estimation

AU - Lim, Pin

AU - Goh, Chi Keong

AU - Tan, Kay Chen

PY - 2016/10/31

Y1 - 2016/10/31

N2 - This paper develops a framework for determining the Remaining Useful Life (RUL) of aero-engines. The framework includes the following modular components: creating a moving time window, a suitable feature extraction method and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on the publicly available C-MAPSS dataset. The prognostic accuracy of the proposed algorithm is also compared against other state-of-the-art methods available in the literature and it has been shown that the proposed framework has the best overall performance.

AB - This paper develops a framework for determining the Remaining Useful Life (RUL) of aero-engines. The framework includes the following modular components: creating a moving time window, a suitable feature extraction method and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on the publicly available C-MAPSS dataset. The prognostic accuracy of the proposed algorithm is also compared against other state-of-the-art methods available in the literature and it has been shown that the proposed framework has the best overall performance.

KW - C-MAPSS

KW - Feature Extraction

KW - Moving Time Window

KW - Neural Networks (NN)

KW - Prognostics

KW - RUL Estimation

UR - https://www.scopus.com/pages/publications/85007155038

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85007155038&origin=recordpage

U2 - 10.1109/IJCNN.2016.7727410

DO - 10.1109/IJCNN.2016.7727410

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781509006199

VL - 2016-October

SP - 1746

EP - 1753

BT - Proceedings of the International Joint Conference on Neural Networks

PB - IEEE

T2 - 2016 International Joint Conference on Neural Networks (IJCNN 2016)

Y2 - 24 July 2016 through 29 July 2016

ER -

A time window neural network based framework for Remaining Useful Life estimation

Abstract

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Research Keywords

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