Rahim, S.A. and Manson, G. (2021) Kernel principal component analysis for structural health monitoring and damage detection of an engineering structure under operational loading variations. Journal of Failure Analysis and Prevention, 21 (6). pp. 1981-1990. ISSN 1547-7029
Abstract
This paper highlights kernel principal component analysis (KPCA) in distinguishing damage-sensitive features from the effects of liquid loading on frequency response. A vibration test is performed on an aircraft wing box incorporated with a liquid tank that undergoes various tank loading. Such experiment is established as a preliminary study of an aircraft wing that undergoes operational load change in a fuel tank. The operational loading effects in a mechanical system can lead to a false alarm as loading and damage effects produce a similar reduction in the vibration response. This study proposes a non-nonlinear transformation to separate loading effects from damage-sensitive features. Based on a baseline data set built from a healthy structure that undergoes systematic tank loading, the Gaussian parameter is measured based on the distance of the baseline data set to various damage states. As a result, both loading and damage features expand and are distinguished better. For novelty damage detection, Mahalanobis square distance (MSD) and Monte Carlo-based threshold are applied. The main contribution of this project is the nonlinear PCA projection to understand the dynamic behavior of the wing box under damage and loading influences and to differentiate both effects that arise from the tank loading and damage severities.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2021. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | Kernel PCA; Mahalanobis squared distance; Euclidean distance; Vibration-based damaged detection; Structural health monitoring |
Dates: |
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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: | 11 Jan 2022 11:47 |
Last Modified: | 11 Jan 2022 11:47 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1007/s11668-021-01260-1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182277 |
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