Helicopter vibration sensor selection using data visualisation

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Abstract

The main objective of the project is to enhance the already effective health-monitoring system (HUMS) for helicopters by analysing structural vibrations to recognise different flight conditions directly from sensor information. The goal of this paper is to develop a new method to select those sensors and frequency bands that are best for detecting changes in flight conditions. We projected frequency information to a 2-dimensional space in order to visualise flight-condition transitions using the Generative Topographic Mapping (GTM) and a variant which supports simultaneous feature selection. We created an objective measure of the separation between different flight conditions in the visualisation space by calculating the Kullback-Leibler (KL) divergence between Gaussian mixture models (GMMs) fitted to each class: the higher the KL-divergence, the better the interclass separation. To find the optimal combination of sensors, they were considered in pairs, triples and groups of four sensors. The sensor triples provided the best result in terms of KL-divergence. We also found that the use of a variational training algorithm for the GMMs gave more reliable results.

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  • Helicopter vibration sensor selection

    Rights statement: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    Accepted author manuscript, 720 KB, PDF-document

Details

Publication dateJul 2012
Publication titleIEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-4673-1025-3
ISBN (Print)978-1-4673-1024-6
Original languageEnglish
EventIEEE International Workshop on Machine Learning for Signal Processing (MLSP 2012) - Santander, Spain

Publication series

NameMachine learning for signal processing
PublisherIEEE
ISSN (Print)1551-2541

Conference

ConferenceIEEE International Workshop on Machine Learning for Signal Processing (MLSP 2012)
CountrySpain
CitySantander
Period23/09/1226/09/12

Bibliographic note

© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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