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

Jorge Ribeiro, Pedro Andrade, Manuel Carvalho, Catarina Silva, Bernardete Ribeiro and Licínio Roque
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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

Mathematics, 2022, vol. 10, issue 9, 1-20

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.

Keywords: design; remaining useful life; visualisation; machine learning; reinforcement learning; condition based maintenance; aircraft maintenance planning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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