Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges

Matthaiou, I., Khandelwal, B. and Antoniadou, I. (2017) Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges. Frontiers in Built Environment, 3. 54.

Abstract

Metadata

Authors/Creators:
  • Matthaiou, I.
  • Khandelwal, B.
  • Antoniadou, I.
Copyright, Publisher and Additional Information: © 2017 Matthaiou, Khandelwal and Antoniadou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: engine condition monitoring; vibration analysis; novelty detection; pattern recognition, one-class support vector machine, wavelets, kernel principal component analysis
Dates:
  • Published: 20 September 2017
  • Published (online): 20 September 2017
  • Accepted: 29 August 2017
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 07 Feb 2018 10:23
Last Modified: 07 Feb 2018 10:25
Published Version: https://doi.org/10.3389/fbuil.2017.00054
Status: Published
Publisher: Frontiers Media
Refereed: Yes
Identification Number: https://doi.org/10.3389/fbuil.2017.00054

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