Inference of helicopter airframe condition

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

Research output: Chapter in Book/Published conference outputConference publication


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 %).

Original languageEnglish
Title of host publication2013 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
Number of pages6
ISBN (Print)978-1-4799-1180-6
Publication statusPublished - 2013
Event16th IEEE international workshop on Machine Learning for Signal Processing - Southampton, United Kingdom
Duration: 22 Sept 201325 Sept 2013

Publication series

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


Workshop16th IEEE international workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP 2013
Country/TerritoryUnited Kingdom


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


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