Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network

Mei Yuan; Yuting Wu; Li Lin

doi:10.1109/AUS.2016.7748035

Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network

Mei Yuan^*
, Yuting Wu
, Li Lin

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

422 Scopus citations

Abstract

Aero engine is a kind of sophisticated and expensive industrial product. Accurate fault location and Remaining Useful Life (RUL) estimation for aero engine can lead to appropriate maintenance actions to avoid catastrophic failures and minimize economic losses. The aim of this paper is to propose utilizing Long Short-Term Memory (LSTM) neural network to get good diagnosis and prediction performance in the cases of complicated operations, hybrid faults and strong noises. The whole proposition is demonstrated and discussed by carrying out tests on a health monitoring dataset of aircraft turbofan engines provided by NASA. Performances of LSTM and some of its modifications were tested and contrasted. Experiment results show the standard LSTM outperformed others.

Original language	English
Title of host publication	AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	135-140
Number of pages	6
ISBN (Electronic)	9781509010875
DOIs	https://doi.org/10.1109/AUS.2016.7748035
State	Published - 17 Nov 2016
Event	2016 IEEE/CSAA International Conference on Aircraft Utility Systems, AUS 2016 - Beijing, China Duration: 10 Oct 2016 → 12 Oct 2016

Publication series

Name	AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems

Conference

Conference	2016 IEEE/CSAA International Conference on Aircraft Utility Systems, AUS 2016
Country/Territory	China
City	Beijing
Period	10/10/16 → 12/10/16

Access to Document

10.1109/AUS.2016.7748035

Cite this

Yuan, M., Wu, Y., & Lin, L. (2016). Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network. In AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems (pp. 135-140). Article 7748035 (AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AUS.2016.7748035

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title = "Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network",

abstract = "Aero engine is a kind of sophisticated and expensive industrial product. Accurate fault location and Remaining Useful Life (RUL) estimation for aero engine can lead to appropriate maintenance actions to avoid catastrophic failures and minimize economic losses. The aim of this paper is to propose utilizing Long Short-Term Memory (LSTM) neural network to get good diagnosis and prediction performance in the cases of complicated operations, hybrid faults and strong noises. The whole proposition is demonstrated and discussed by carrying out tests on a health monitoring dataset of aircraft turbofan engines provided by NASA. Performances of LSTM and some of its modifications were tested and contrasted. Experiment results show the standard LSTM outperformed others.",

author = "Mei Yuan and Yuting Wu and Li Lin",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE/CSAA International Conference on Aircraft Utility Systems, AUS 2016 ; Conference date: 10-10-2016 Through 12-10-2016",

year = "2016",

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doi = "10.1109/AUS.2016.7748035",

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series = "AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems",

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Yuan, M, Wu, Y & Lin, L 2016, Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network. in AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems., 7748035, AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems, Institute of Electrical and Electronics Engineers Inc., pp. 135-140, 2016 IEEE/CSAA International Conference on Aircraft Utility Systems, AUS 2016, Beijing, China, 10/10/16. https://doi.org/10.1109/AUS.2016.7748035

Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network. / Yuan, Mei; Wu, Yuting; Lin, Li.
AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems. Institute of Electrical and Electronics Engineers Inc., 2016. p. 135-140 7748035 (AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Lin, Li

PY - 2016/11/17

Y1 - 2016/11/17

N2 - Aero engine is a kind of sophisticated and expensive industrial product. Accurate fault location and Remaining Useful Life (RUL) estimation for aero engine can lead to appropriate maintenance actions to avoid catastrophic failures and minimize economic losses. The aim of this paper is to propose utilizing Long Short-Term Memory (LSTM) neural network to get good diagnosis and prediction performance in the cases of complicated operations, hybrid faults and strong noises. The whole proposition is demonstrated and discussed by carrying out tests on a health monitoring dataset of aircraft turbofan engines provided by NASA. Performances of LSTM and some of its modifications were tested and contrasted. Experiment results show the standard LSTM outperformed others.

AB - Aero engine is a kind of sophisticated and expensive industrial product. Accurate fault location and Remaining Useful Life (RUL) estimation for aero engine can lead to appropriate maintenance actions to avoid catastrophic failures and minimize economic losses. The aim of this paper is to propose utilizing Long Short-Term Memory (LSTM) neural network to get good diagnosis and prediction performance in the cases of complicated operations, hybrid faults and strong noises. The whole proposition is demonstrated and discussed by carrying out tests on a health monitoring dataset of aircraft turbofan engines provided by NASA. Performances of LSTM and some of its modifications were tested and contrasted. Experiment results show the standard LSTM outperformed others.

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BT - AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems

PB - Institute of Electrical and Electronics Engineers Inc.

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Yuan M, Wu Y, Lin L. Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network. In AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems. Institute of Electrical and Electronics Engineers Inc. 2016. p. 135-140. 7748035. (AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems). doi: 10.1109/AUS.2016.7748035

Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network

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