Helicopter vibration sensor selection using data visualisation

Waljinder S. Gill, Ian T. Nabney, D. Wells

Research output: Chapter in Book/Published conference outputConference publication


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.
Original languageEnglish
Title of host publicationIEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012
Number of pages6
ISBN (Electronic)978-1-4673-1025-3
ISBN (Print)978-1-4673-1024-6
Publication statusPublished - Jul 2012
EventIEEE International Workshop on Machine Learning for Signal Processing (MLSP 2012) - Santander, Spain
Duration: 23 Sept 201226 Sept 2012

Publication series

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


ConferenceIEEE International Workshop on Machine Learning for Signal Processing (MLSP 2012)

Bibliographical note

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