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International Journal of Turbo & Jet-Engines

Ed. by Sherbaum, Valery / Erenburg, Vladimir


IMPACT FACTOR 2018: 0.863

CiteScore 2018: 0.66

SCImago Journal Rank (SJR) 2018: 0.211
Source Normalized Impact per Paper (SNIP) 2018: 0.625

Online
ISSN
2191-0332
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Volume 35, Issue 4

Issues

Gas Turbine Engine Gas-path Fault Diagnosis Based on Improved SBELM Architecture

Feng Lu
  • Corresponding author
  • Jiangsu Province Key Laboratory of Aerospace Power Systems, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Collaborative Innovation Center of advanced Aero-Engine, Beijing 100191, China
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Jipeng Jiang
  • Jiangsu Province Key Laboratory of Aerospace Power Systems, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Jinquan Huang
  • Jiangsu Province Key Laboratory of Aerospace Power Systems, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Collaborative Innovation Center of advanced Aero-Engine, Beijing 100191, China
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2018-11-08 | DOI: https://doi.org/10.1515/tjj-2016-0050

Abstract

Various model-based methods are widely used to aircraft engine fault diagnosis, and an accurate engine model is used in these approaches. However, it is difficult to obtain general engine model with high accuracy due to engine individual difference, lifecycle performance deterioration and modeling uncertainty. Recently, data-driven diagnostic approaches for aircraft engine become more popular with the development of machine learning technologies. While these data-driven methods to engine fault diagnosis tend to ignore experimental data sparse and uncertainty, which results in hardly achieve fast fault diagnosis for multiple patterns. This paper presents a novel data-driven diagnostic approach using Sparse Bayesian Extreme Learning Machine (SBELM) for engine fault diagnosis. This methodology addresses fast fault diagnosis without relying on engine model. To enhance the reliability of fast fault diagnosis and enlarge the detectable fault number, a SBELM-based multi-output classifier framework is designed. The reduced sparse topology of ELM is presented and utilized to fault diagnosis extended from single classifier to multi-output classifier. The effects of noise and measurement uncertainty are taken into consideration. Simulation results show the SBELM-based multi-output classifier for engine fault diagnosis is superior to the existing data-driven ones with regards to accuracy and computational efforts.

Keywords: aircraft engine; gas-path fault diagnosis; extreme learning machine (ELM); sparse Bayesian; measurement uncertainty

PACS: 47.85.Gj

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About the article

Received: 2016-07-21

Accepted: 2016-09-01

Published Online: 2018-11-08

Published in Print: 2018-12-19


This paper is supported by National Nature Science Foundation of China (No.61304113), Jiangsu Province Nature Science Foundation (No. BK20130802), and China Outstanding Postdoctoral Science Foundation (No.2015T80552).[Correction added after ahead-of-print publication on 20 September 2016: The Funding number of the sponsor National Nature Science Foundation was updated from No.61304133 to No.61304113.]


Citation Information: International Journal of Turbo & Jet-Engines, Volume 35, Issue 4, Pages 351–363, ISSN (Online) 2191-0332, ISSN (Print) 0334-0082, DOI: https://doi.org/10.1515/tjj-2016-0050.

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