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Artificial neural networks for PIO events classification comparing different data collection procedures

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Abstract

This work evaluates the accuracy and reliability of PIO test classification using the PIO Rating Scale and proposes using an automatic tool for this evaluation based on test data to eliminate the subjectivity inherent to the application of rating scales. Two test procedures (discrete synthetic task and pitch capture) are executed in a flight simulator, using aircraft dynamic models with different PIO proneness and experienced flight test pilots. The results show a significant effect of subjectivity in pilot rating and various reliability for different test procedures. This data is used to build an artificial neural network (ANN) proposed to classify the executions using the PIO Rating Scale. The ANN presented low computational cost and 97.1% accuracy when using data extracted from the pitch capture test procedure.

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Acknowledgements

The authors thanks EMBRAER S.A. to make available some of the flight test pilots to perform the tests for this work.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not for-profit sectors.

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Authors and Affiliations

  1. Department of Aeronautics Engineering, University of São Paulo, São Carlos, São Paulo, 13563-120, Brazil

    Adriano Ghigiarelli Bruschi & Jorge Henrique Bidinotto

  2. EMBRAER S.A, São José Dos Campos, São Paulo, 12227-901, Brazil

    Daniel Drewiacki

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  1. Adriano Ghigiarelli Bruschi
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  3. Jorge Henrique Bidinotto
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Correspondence to Jorge Henrique Bidinotto.

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Bruschi, A.G., Drewiacki, D. & Bidinotto, J.H. Artificial neural networks for PIO events classification comparing different data collection procedures. J Braz. Soc. Mech. Sci. Eng. 46, 496 (2024). https://doi.org/10.1007/s40430-024-05070-y

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