Prediction of Landing Gear Loads Using Machine Learning Techniques

Geoffrey K T Holmes, Pia N Sartor, Stephen Reed, Paul J D Southern, Keith Worden, Elizabeth Cross

Research output: Contribution to journalArticle (Academic Journal)peer-review

18 Citations (Scopus)
211 Downloads (Pure)

Abstract

This paper investigates the feasibility of using machine learning algorithms to predict the loads experienced by a landing gear during landing. For this purpose, results on drop test data and flight test data will be examined. This paper will focus on the use of Gaussian Process regression for the prediction of loads on 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 the 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 GP 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 by 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 is 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.
Original languageEnglish
Pages (from-to)568-582
Number of pages15
JournalSTRUCTURAL HEALTH MONITORING
Volume15
Issue number5
Early online date11 Jul 2016
DOIs
Publication statusPublished - Sep 2016

Keywords

  • Landing Gear
  • Loads
  • Machine Learning
  • Gaussian Process Regression

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