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AI-Driven Predictive Maintenance for Autonomous Vehicles for Product-Service System 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: 0000-0002-7741-6405
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: 0000-0003-4875-391X
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2024 (English)In: Procedia CIRP / [ed] Erkoyuncu J.A., Farsi M., Addepalli P., Elsevier, 2024, Vol. 128, p. 84-89Conference paper, Published paper (Refereed)
Abstract [en]

The paper presents an Artificial Intelligence-driven approach to predictive maintenance for Product-Service System (PSS) development. This study focuses on time-based and condition-based maintenance, leveraging variational autoencoders to identify both predicted and unpredicted maintenance issues in autonomous haulers. By analyzing data patterns and forecasting future values, this approach enables proactive maintenance and informed decision-making in the early stages of PSS development. 

The inclusion of interaction terms enhances the model’s ability to capture the interdependencies among system components, addressing hidden failure modes. Comprehensive evaluations demonstrate the effectiveness and robustness of the developed models, showcasing resilience to noise and variations in operational data. 

The integration of predictive maintenance with PSS development offers a strategic advantage, providing insights into vehicle performance early in the development phases. This empowers decision-makers for efficient resource allocation and proactive maintenance planning. The research highlights the limitations and potential areas of improvement while also emphasizing the practical applicability and significance of the developed models in enhancing PSS development. 

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 128, p. 84-89
Series
Procedia CIRP, E-ISSN 2212-8271
Keywords [en]
Predictive Maintenance; Autonomous Haulers; Product-Service Systems; Artificial Intelligence; Decision-Making.
National Category
Other Mechanical Engineering
Research subject
Mechanical Engineering; Mechanical Engineering
Identifiers
URN: urn:nbn:se:bth-26158DOI: 10.1016/j.procir.2024.06.008Scopus ID: 2-s2.0-85208789052OAI: oai:DiVA.org:bth-26158DiVA, id: diva2:1855819
Conference
34th CIRP Design Conference, CIRP 2024, Cranfield, June 3-5, 2024
Part of project
ASPECT – A System for Electric and Connected Transport Solutions, VinnovaAvailable from: 2024-05-03 Created: 2024-05-03 Last updated: 2024-11-22Bibliographically 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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Aeddula, OmsriFrank, MartinRuvald, RyanJohansson Askling, ChristianWall, JohanLarsson, Tobias

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