Intelligent acoustic rotor speed estimation for an autonomous helicopter

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PASSOW, Benjamin N., GONGORA, Mario A., HOPGOOD, Adrian A. and SMITH, Sophy (2012). Intelligent acoustic rotor speed estimation for an autonomous helicopter. Applied Soft Computing, 12 (11), 3313-3324.

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Official URL: http://dx.doi.org/10.1016/j.asoc.2012.05.022
Link to published version:: https://doi.org/10.1016/j.asoc.2012.05.022
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    Abstract

    Acoustic sensing to gather information about a machine can be highly beneficial, but processing the data can be difficult. In this work, a variety of methodologies have been studied to extract rotor speed information from the sound signature of an autonomous helicopter, with no a-priori knowledge of its underlying acoustic properties. The autonomous helicopter has two main rotors that are mostly identical. In order to identify the rotors’ speeds individually, a comparative evaluation has been made of learning methods with input selection, reduction and aggregation methods. The resulting estimators have been tested on unseen training data as well as in actual free-flight tests. The best results were found by using a genetic algorithm to identify the important frequency bands, a centroid method to aggregate the bands, and a neural-network estimator of the rotor speeds. This approach succeeded in estimating individual rotor speeds of an autonomous helicopter without being distracted by the other, mainly identical, rotor. These results were achieved using standard, low-cost hardware, and a learning approach that did not require any a-priori knowledge about the system's acoustic properties.

    Item Type: Article
    Research Institute, Centre or Group - Does NOT include content added after October 2018: Materials and Engineering Research Institute > Modelling Research Centre > Microsystems and Machine Vision Laboratory
    Identification Number: https://doi.org/10.1016/j.asoc.2012.05.022
    Page Range: 3313-3324
    Depositing User: Adrian Hopgood
    Date Deposited: 24 Sep 2012 13:38
    Last Modified: 19 Mar 2021 00:01
    URI: https://shura.shu.ac.uk/id/eprint/6225

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