Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/100502 https://doi.org/10.3390/math10091604 |
Resumo: | 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. |
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Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisationdesignremaining useful lifevisualisationmachine learningreinforcement learningcondition based maintenanceaircraft maintenance planningAircraft 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.2022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/100502http://hdl.handle.net/10316/100502https://doi.org/10.3390/math10091604eng2227-7390Ribeiro, JorgeAndrade, PedroCarvalho, Manuel CostaSilva, CatarinaRibeiro, BernardeteRoque, Licínioinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-11-29T15:56:22Zoai:estudogeral.uc.pt:10316/100502Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:17:52.668770Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation |
title |
Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation |
spellingShingle |
Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation Ribeiro, Jorge design remaining useful life visualisation machine learning reinforcement learning condition based maintenance aircraft maintenance planning |
title_short |
Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation |
title_full |
Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation |
title_fullStr |
Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation |
title_full_unstemmed |
Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation |
title_sort |
Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation |
author |
Ribeiro, Jorge |
author_facet |
Ribeiro, Jorge Andrade, Pedro Carvalho, Manuel Costa Silva, Catarina Ribeiro, Bernardete Roque, Licínio |
author_role |
author |
author2 |
Andrade, Pedro Carvalho, Manuel Costa Silva, Catarina Ribeiro, Bernardete Roque, Licínio |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Ribeiro, Jorge Andrade, Pedro Carvalho, Manuel Costa Silva, Catarina Ribeiro, Bernardete Roque, Licínio |
dc.subject.por.fl_str_mv |
design remaining useful life visualisation machine learning reinforcement learning condition based maintenance aircraft maintenance planning |
topic |
design remaining useful life visualisation machine learning reinforcement learning condition based maintenance aircraft maintenance planning |
description |
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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/100502 http://hdl.handle.net/10316/100502 https://doi.org/10.3390/math10091604 |
url |
http://hdl.handle.net/10316/100502 https://doi.org/10.3390/math10091604 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2227-7390 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134074295549952 |