AI-Driven Comprehension of Autonomous Construction Equipment Behavior for Improved PSS Development

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dc.contributor.authorAeddula, Omsri
dc.contributor.authorRuvald, Ryan
dc.contributor.authorWall, Johan
dc.contributor.authorLarsson, Tobias
dc.date.accessioned2023-12-26T18:36:38Z
dc.date.available2023-12-26T18:36:38Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.123
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.otherd278a785-4ef4-4f22-9f73-05096f9231c3
dc.identifier.urihttps://hdl.handle.net/10125/106500
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData-driven Services and Servitization in Manufacturing: Innovation, Engineering, Transformation, and Management
dc.subjectartificial intelligence
dc.subjectautonomous machine
dc.subjectmachine behavior.
dc.subjectproduct-service system
dc.subjectprototyping
dc.titleAI-Driven Comprehension of Autonomous Construction Equipment Behavior for Improved PSS Development
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractThis paper presents an approach that utilizes artificial intelligence techniques to identify autonomous machine behavior patterns. The context for investigation involves a fleet of prototype autonomous haulers as part of a Product Service System solution under development in the construction and mining industry. The approach involves using deep learning-based object detection and computer vision to understand how prototype machines operate in different situations. The trained model accurately predicts and tracks the loaded and unloaded machines and helps to identify the data patterns such as course deviations, machine failures, unexpected slowdowns, battery life, machine activity, number of cycles per charge, and speed. PSS solutions hinge on efficiently allocating resources to meet the required site-level output. Solution providers can make more informed decisions at the earlier stages of development by using the AI techniques outlined in the paper, considering asset management and reallocation of resources to account for unplanned stoppages or unexpected slowdowns. Understanding machine behavioral aspects in early-stage PSS development could enable more efficient and customized PSS solutions.
dcterms.extent10 pages
prism.startingpage1017

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Data-driven Services and Servitization in Manufacturing: Innovation, Engineering, Transformation, and Management