@inproceedings{94d9c92691af42a6ae0acc7d64c0c031,
title = "Inference of helicopter airframe condition",
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 %).",
keywords = "condition monitoring, flight condition, non-linear model, signal processing, switching state space, vibration",
author = "Gill, {Waljinder S.} and Nabney, {Ian T.} and Daniel Wells",
year = "2013",
doi = "10.1109/MLSP.2013.6661960",
language = "English",
isbn = "978-1-4799-1180-6",
series = "Machine learning for signal processing",
publisher = "IEEE",
editor = "Saeid Sanei and Paris Smaragdis and Asoke Nandi and {et al}",
booktitle = "2013 IEEE International Workshop on Machine Learning for Signal Processing September 22-25, Southampton, United Kingdom. Proceedings MLSP2013",
address = "United States",
note = "16th IEEE international workshop on Machine Learning for Signal Processing, MLSP 2013 ; Conference date: 22-09-2013 Through 25-09-2013",
}