Inference of helicopter airframe condition

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

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

1 Citation (Scopus)


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
Place of PublicationUnited States
PublisherIEEE Computer Society
Number of pages6
ISBN (Print)978-1-4799-1180-6
Publication statusPublished - 2013

Publication series

NameMachine learning for signal processing


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


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