Helicopter vibration sensor selection using data visualisation

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

doi:10.1109/MLSP.2012.6349808

Helicopter vibration sensor selection using data visualisation

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

Computer Science Research Group

Research output: Chapter in Book/Published conference output › Conference publication

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.

Original language	English
Title of host publication	IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	978-1-4673-1025-3
ISBN (Print)	978-1-4673-1024-6
DOIs	https://doi.org/10.1109/MLSP.2012.6349808
Publication status	Published - Jul 2012
Event	IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2012) - Santander, Spain Duration: 23 Sept 2012 → 26 Sept 2012

Publication series

Name	Machine learning for signal processing
Publisher	IEEE
ISSN (Print)	1551-2541

Conference

Conference	IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2012)
Country/Territory	Spain
City	Santander
Period	23/09/12 → 26/09/12

Bibliographical 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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10.1109/MLSP.2012.6349808

Helicopter vibration sensor selection
© 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

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6349808

Cite this

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title = "Helicopter vibration sensor selection using data visualisation",

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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Gill, WS, Nabney, IT & Wells, D 2012, Helicopter vibration sensor selection using data visualisation. in IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012. Machine learning for signal processing, IEEE, IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2012), Santander, Spain, 23/09/12. https://doi.org/10.1109/MLSP.2012.6349808

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N2 - 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 using data visualisation

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