Inference of helicopter airframe condition

Research output: Chapter in Book/Report/Conference proceedingConference contribution

View graph of relations Save citation

Authors

Research units

Abstract

The goal of this paper is to model normal airframe conditions for helicopters in order to detect changes. This is done by inferring the flying state using a selection of sensors and frequency bands that are best for discriminating between different states. We used non-linear state-space models (NLSSM) for modelling flight conditions based on short-time frequency analysis of the vibration data and embedded the models in a switching framework to detect transitions between states. We then created a density model (using a Gaussian mixture model) for the NLSSM innovations: this provides a model for normal operation. To validate our approach, we used data with added synthetic abnormalities which was detected as low-probability periods. The model of normality gave good indications of faults during the flight, in the form of low probabilities under the model, with high accuracy (>92 %).

Request a copy

Request a copy

Details

Publication date2013
Publication title2013 IEEE International Workshop on Machine Learning for Signal Processing September 22-25, Southampton, United Kingdom. Proceedings MLSP2013
EditorsSaeid Sanei, Paris Smaragdis, Asoke Nandi, et al
PublisherIEEE
Number of pages6
ISBN (Print)978-1-4799-1180-6
Original languageEnglish
Event16th IEEE international workshop on Machine Learning for Signal Processing - Southampton, United Kingdom

Publication series

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

Workshop

Workshop16th IEEE international workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP 2013
CountryUnited Kingdom
CitySouthampton
Period22/09/1325/09/13

    Keywords

  • condition monitoring, flight condition, non-linear model, signal processing, switching state space, vibration

DOI

Employable Graduates; Exploitable Research

Copy the text from this field...