Helicopter vibration sensor selection using data visualisation


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.

Publication DOI: https://doi.org/10.1109/MLSP.2012.6349808
Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Event Title: IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2012)
Event Type: Other
Event Dates: 2012-09-23 - 2012-09-26
ISBN: 978-1-4673-1024-6, 978-1-4673-1025-3
Last Modified: 28 Jun 2024 08:50
Date Deposited: 30 Jan 2013 10:48
Full Text Link: http://ieeexplo ... rnumber=6349808
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2012-07
Authors: Gill, Waljinder S.
Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)
Wells, D.



Version: Accepted Version

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