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AI-Driven Comprehension of Autonomous Construction Equipment Behavior for Improved PSS Development
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. (PDRL - Product Development Research Lab)ORCID iD: 0000-0002-3876-5602
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. (PDRL - Product Development Research Lab)ORCID iD: 0000-0003-0056-4562
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. (PDRL - Product Development Research Lab)ORCID iD: 0000-0002-7804-7306
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. (PDRL - Product Development Research Lab)ORCID iD: 0000-0002-9662-4576
2024 (English)In: Proceedings of the 57th Annual Hawaii International Conference on System Sciences, IEEE Computer Society, 2024, p. 1017-1026Conference paper, Published paper (Refereed)
Abstract [en]

This 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.

Place, publisher, year, edition, pages
IEEE Computer Society, 2024. p. 1017-1026
Series
Proceedings of the Hawaii International Conference on System Sciences, E-ISSN 2572-6862
Keywords [en]
Product-Service System, Deep Learning, Autonomous Machine, Prototyping, Machine Behavior.
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Mechanical Engineering
Identifiers
URN: urn:nbn:se:bth-25863Scopus ID: 2-s2.0-85199798341ISBN: 9780998133171 (electronic)OAI: oai:DiVA.org:bth-25863DiVA, id: diva2:1824596
Conference
57th Annual Hawaii International Conference on System Sciences, Hilton Hawaiian Village Waikiki Beach Resort, January 3-6, 2024
Part of project
ASPECT – A System for Electric and Connected Transport Solutions, Vinnova
Funder
Vinnova, 2021-04347Available from: 2024-01-06 Created: 2024-01-06 Last updated: 2024-08-13Bibliographically approved
In thesis
1. Navigating Data Challenges: AI-Driven Decision Support for Product-Service System Development
Open this publication in new window or tab >>Navigating Data Challenges: AI-Driven Decision Support for Product-Service System Development
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Solution providers are transitioning from product-centric models to service-oriented solutions. This shift has led to the rise of Product-Service Systems (PSS), which offer a holistic approach by integrating physical products with associated services. However, the inherent complexity and collaborative nature of PSS development present a significant challenge: information gathering, analysis, and knowledge building. This is further amplified in the early stages of PSS development due to data challenges such as uncertainty, ambiguity, and complexity. This complicates informed decision-making, potentially leading to the risk of sub-optimal outcomes and impacting the success of final offerings.

This research proposes an AI-powered data analysis approach to address these data challenges and augment the decision-making process of PSS development. The focus is on supporting early-stage decision-making, as decisions made at this stage greatly impact the success of final solutions. The research investigates how data can be utilized and visualized to extract actionable insights, ultimately facilitating informed decision-making.

The presented research demonstrates that AI-powered data analysis effectively supports informed decision-making in early-stage PSS development. By extracting actionable insights from complex data, handling data limitations, and enabling informed strategic decisions, knowledge sharing, and collaboration are facilitated among stakeholders. Furthermore, integrating AI with visualization tools fosters knowledge building and a deeper understanding of system behavior, ultimately leading to more successful PSS solutions. The efficacy of AI-powered data analysis for handling diverse data types across application domains is demonstrated, potentially leading to benefits such as a deeper understanding of system behavior and proactive solution strategies. These advancements contribute to developing decision support systems specifically for PSS development.

Overall, this research demonstrates the efficacy of AI-powered data analysis in overcoming data challenges and empowering decision-makers in early-stage PSS development. This translates to more informed choices, leading to the creation of successful and efficient PSS solutions.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2024
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2024:11
Keywords
Product-Service System (PSS), Artificial Intelligence, Early-stage Decision Support, Data Challenges, Informed Decision-making
National Category
Mechanical Engineering Other Engineering and Technologies
Research subject
Mechanical Engineering
Identifiers
urn:nbn:se:bth-26162 (URN)978-91-7295-484-7 (ISBN)
Public defence
2024-06-14, J1630, Campus Gräsvik, Karlskrona, 09:30 (English)
Opponent
Supervisors
Available from: 2024-05-08 Created: 2024-05-07 Last updated: 2025-02-10Bibliographically approved

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HICSS_Omsri(672 kB)161 downloads
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Scopushttps://hdl.handle.net/10125/106500

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Aeddula, OmsriRuvald, RyanWall, JohanLarsson, Tobias

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