A Remaining Useful Life Prediction Framework for Aero-engine Using Information Entropy-based Criterion and PCA-RVM

X. Li; L. Yang; X. Liu; F. Zhu

doi:10.1109/ICRMS55680.2022.9944571

A Remaining Useful Life Prediction Framework for Aero-engine Using Information Entropy-based Criterion and PCA-RVM

X. Li, L. Yang^*, X. Liu, F. Zhu

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

To deal with the challenge of feature selection and extraction in the remaining useful life (RUL) prediction for aero-engines, this paper proposes a framework using multi-sensors data, which involves three key components (i) an information entropy-based criterion for sensor selection, (ii) principal component analysis (PCA) for the construction of synthesized health index, and (iii) relevance vector machine (RVM)-based RUL prediction. The proposed method combines the PCA with RVM and improves the prediction accuracy by employing a novel entropy-based criterion for sensor selection. The effectiveness of this approach is demonstrated and validated with the turbofan engine released by NASA Research Center.

Original language	English
Title of host publication	2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS)
Publisher	IEEE
Pages	270-274
ISBN (Electronic)	978-1-6654-8690-3
DOIs	https://doi.org/10.1109/ICRMS55680.2022.9944571
Publication status	Published - 2022
Event	13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022 - Hong Kong, China Duration: 21 Aug 2022 → 24 Aug 2022 http://www.icrms2022.org

Publication series

Name	13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022

Conference

Conference	13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022
Place	China
City	Hong Kong
Period	21/08/22 → 24/08/22
Internet address	http://www.icrms2022.org

Research Keywords

aero-engine
entropy
principal component analysis
relevance vector machine
Remaining useful life prediction

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10.1109/ICRMS55680.2022.9944571

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Li, X., Yang, L., Liu, X., & Zhu, F. (2022). A Remaining Useful Life Prediction Framework for Aero-engine Using Information Entropy-based Criterion and PCA-RVM. In 2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS) (pp. 270-274). (13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022). IEEE. https://doi.org/10.1109/ICRMS55680.2022.9944571

Li, X. ; Yang, L. ; Liu, X. et al. / A Remaining Useful Life Prediction Framework for Aero-engine Using Information Entropy-based Criterion and PCA-RVM. 2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS). IEEE, 2022. pp. 270-274 (13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022).

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title = "A Remaining Useful Life Prediction Framework for Aero-engine Using Information Entropy-based Criterion and PCA-RVM",

abstract = "To deal with the challenge of feature selection and extraction in the remaining useful life (RUL) prediction for aero-engines, this paper proposes a framework using multi-sensors data, which involves three key components (i) an information entropy-based criterion for sensor selection, (ii) principal component analysis (PCA) for the construction of synthesized health index, and (iii) relevance vector machine (RVM)-based RUL prediction. The proposed method combines the PCA with RVM and improves the prediction accuracy by employing a novel entropy-based criterion for sensor selection. The effectiveness of this approach is demonstrated and validated with the turbofan engine released by NASA Research Center.",

keywords = "aero-engine, entropy, principal component analysis, relevance vector machine, Remaining useful life prediction",

author = "X. Li and L. Yang and X. Liu and F. Zhu",

year = "2022",

doi = "10.1109/ICRMS55680.2022.9944571",

language = "English",

series = "13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022",

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booktitle = "2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS)",

address = "United States",

note = "13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022 ; Conference date: 21-08-2022 Through 24-08-2022",

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Li, X, Yang, L, Liu, X & Zhu, F 2022, A Remaining Useful Life Prediction Framework for Aero-engine Using Information Entropy-based Criterion and PCA-RVM. in 2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS). 13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022, IEEE, pp. 270-274, 13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022, Hong Kong, China, 21/08/22. https://doi.org/10.1109/ICRMS55680.2022.9944571

A Remaining Useful Life Prediction Framework for Aero-engine Using Information Entropy-based Criterion and PCA-RVM. / Li, X.; Yang, L.; Liu, X. et al.
2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS). IEEE, 2022. p. 270-274 (13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022).

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

TY - GEN

T1 - A Remaining Useful Life Prediction Framework for Aero-engine Using Information Entropy-based Criterion and PCA-RVM

AU - Li, X.

AU - Yang, L.

AU - Liu, X.

AU - Zhu, F.

PY - 2022

Y1 - 2022

N2 - To deal with the challenge of feature selection and extraction in the remaining useful life (RUL) prediction for aero-engines, this paper proposes a framework using multi-sensors data, which involves three key components (i) an information entropy-based criterion for sensor selection, (ii) principal component analysis (PCA) for the construction of synthesized health index, and (iii) relevance vector machine (RVM)-based RUL prediction. The proposed method combines the PCA with RVM and improves the prediction accuracy by employing a novel entropy-based criterion for sensor selection. The effectiveness of this approach is demonstrated and validated with the turbofan engine released by NASA Research Center.

AB - To deal with the challenge of feature selection and extraction in the remaining useful life (RUL) prediction for aero-engines, this paper proposes a framework using multi-sensors data, which involves three key components (i) an information entropy-based criterion for sensor selection, (ii) principal component analysis (PCA) for the construction of synthesized health index, and (iii) relevance vector machine (RVM)-based RUL prediction. The proposed method combines the PCA with RVM and improves the prediction accuracy by employing a novel entropy-based criterion for sensor selection. The effectiveness of this approach is demonstrated and validated with the turbofan engine released by NASA Research Center.

KW - aero-engine

KW - entropy

KW - principal component analysis

KW - relevance vector machine

KW - Remaining useful life prediction

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U2 - 10.1109/ICRMS55680.2022.9944571

DO - 10.1109/ICRMS55680.2022.9944571

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

T3 - 13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022

SP - 270

EP - 274

BT - 2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS)

PB - IEEE

T2 - 13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022

Y2 - 21 August 2022 through 24 August 2022

ER -

Li X, Yang L, Liu X, Zhu F. A Remaining Useful Life Prediction Framework for Aero-engine Using Information Entropy-based Criterion and PCA-RVM. In 2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS). IEEE. 2022. p. 270-274. (13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022). Epub 2022 Nov 15. doi: 10.1109/ICRMS55680.2022.9944571

A Remaining Useful Life Prediction Framework for Aero-engine Using Information Entropy-based Criterion and PCA-RVM

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