Holmes, G., Sartor, P., Reed, S. et al. (3 more authors) (2016) Prediction of landing gear loads using machine learning techniques. Structural Health Monitoring, 15 (5). pp. 568-582. ISSN 1475-9217
Abstract
This article investigates the feasibility of using machine learning algorithms to predict the loads experienced by a landing gear during landing. For this purpose, the results on drop test data and flight test data will be examined. This article will focus on the use of Gaussian process regression for the prediction of loads on the components of a landing gear. For the learning task, comprehensive measurement data from drop tests are available. These include measurements of strains at key locations, such as on the side-stay and torque link, as well as acceleration measurements of the drop carriage and the gear itself, measurements of shock absorber travel, tyre closure, shock absorber pressure and wheel speed. Ground-to-tyre loads are also available through measurements made with a drop test ground reaction platform. The aim is to train the Gaussian process to predict load at a particular location from other available measurements, such as accelerations, or measurements of the shock absorber. If models can be successfully trained, then future load patterns may be predicted using only these measurements. The ultimate aim is to produce an accurate model that can predict the load at a number of locations across the landing gear using measurements that are readily available or may be measured more easily than directly measuring strain on the gear itself (for example, these may be measurements already available on the aircraft, or from a small number of sensors attached to the gear). The drop test data models provide a positive feasibility test which is the basis for moving on to the critical task of prediction on flight test data. For this, a wide range of available flight test measurements are considered for potential model inputs (excluding strain measurements themselves), before attempting to refine the model or use a smaller number of measurements for the prediction.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Geoffrey Holmes, Pia Sartor, Stephen Reed, Paul Southern, Keith Worden, and Elizabeth Cross, Prediction of landing gear loads using machine learning techniques, Structural Health Monitoring Vol 15, Issue 5, pp. 568 - 582. Copyright © 2018 SAGE Publications. Reprinted by permission of SAGE Publications. |
Keywords: | Landing gear; loads; machine learning; Gaussian process regression |
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) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research |
Funding Information: | Funder Grant number INNOVATE UK (TSB) 113020 (38287-263191) MESSIER DOWTY B99838 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Jun 2018 10:52 |
Last Modified: | 13 Jun 2018 04:35 |
Published Version: | https://doi.org/10.1177/1475921716651809 |
Status: | Published |
Publisher: | SAGE Publications |
Refereed: | Yes |
Identification Number: | 10.1177/1475921716651809 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129364 |