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Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation

Author

Listed:
  • Jorge Ribeiro

    (CISUC—Centre Informatics and Systems, Informatics Engineering Department, University of Coimbra, 3004-531 Coimbra, Portugal)

  • Pedro Andrade

    (CISUC—Centre Informatics and Systems, Informatics Engineering Department, University of Coimbra, 3004-531 Coimbra, Portugal)

  • Manuel Carvalho

    (CISUC—Centre Informatics and Systems, Informatics Engineering Department, University of Coimbra, 3004-531 Coimbra, Portugal)

  • Catarina Silva

    (CISUC—Centre Informatics and Systems, Informatics Engineering Department, University of Coimbra, 3004-531 Coimbra, Portugal)

  • Bernardete Ribeiro

    (CISUC—Centre Informatics and Systems, Informatics Engineering Department, University of Coimbra, 3004-531 Coimbra, Portugal)

  • Licínio Roque

    (CISUC—Centre Informatics and Systems, Informatics Engineering Department, University of Coimbra, 3004-531 Coimbra, Portugal)

Abstract

Aircraft maintenance is a complex domain where designing new systems that include Machine Learning (ML) algorithms can become a challenge. In the context of designing a tool for Condition-Based Maintenance (CBM) in aircraft maintenance planning, this case study addresses (1) the use of Playful Probing approach to obtain insights that allow understanding of how to design for interaction with ML algorithms, (2) the integration of a Reinforcement Learning (RL) agent for Human–AI collaboration in maintenance planning and (3) the visualisation of CBM indicators. Using a design science research approach, we designed a Playful Probe protocol and materials, and evaluated results by running a participatory design workshop. Our main contribution is to show how to elicit ideas for integration of maintenance planning practices with ML estimation tools and the RL agent. Through a participatory design workshop with participants’ observation, in which they played with CBM artefacts, Playful Probes favour the elicitation of user interaction requirements with the RL planning agent to aid the planner to obtain a reliable maintenance plan and turn possible to understand how to represent CBM indicators and visualise them through a trajectory prediction.

Suggested Citation

  • Jorge Ribeiro & Pedro Andrade & Manuel Carvalho & Catarina Silva & Bernardete Ribeiro & Licínio Roque, 2022. "Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation," Mathematics, MDPI, vol. 10(9), pages 1-20, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1604-:d:811240
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    References listed on IDEAS

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    1. Xiao Wang & Hongwei Wang & Chao Qi, 2016. "Multi-agent reinforcement learning based maintenance policy for a resource constrained flow line system," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 325-333, April.
    2. Stephane R. A. Barde & Soumaya Yacout & Hayong Shin, 2019. "Optimal preventive maintenance policy based on reinforcement learning of a fleet of military trucks," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 147-161, January.
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