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  • 1.
    Bengtsson, Marcus
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation. Volvo Construction Equipment Operations Eskilstuna Sweden, Sweden.
    D'Cruze, Ricky Stanley
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sakao, T.
    Department of Management and Engineering, Linköping University, Sweden.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sohlberg, Rickard
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Combining Ontology and Large Language Models to Identify Recurring Machine Failures in Free-Text Fields2024In: Advances in Transdisciplinary Engineering, IOS Press BV , 2024, Vol. 52, p. 27-38Conference paper (Refereed)
    Abstract [en]

    Companies must enhance total maintenance effectiveness to stay competitive, focusing on both digitalization and basic maintenance procedures. Digitalization offers technologies for data-driven decision-making, but many maintenance decisions still lack a factual basis. Prioritizing efficiency and effectiveness require analyzing equipment history, facilitated by using Computerized Maintenance Management Systems (CMMS). However, CMMS data often contains unstructured free-text, leading to manual analysis, which is resource-intensive and reactive, focusing on short time periods and specific equipment. Two approaches are available to solve the issue: minimizing free-text entries or using advanced methods for processing them. Free-text allows detailed descriptions but may lack completeness, while structured reporting aids automated analysis but may limit fault description richness. As knowledge and experience are vital assets for companies this research uses a hybrid approach by combining Natural Language Processing with domain specific ontology and Large Language Models to extract information from free-text entries, enabling the possibility of real-time analysis e.g., identifying recurring failure and knowledge sharing across global sites.

  • 2.
    D'Cruze, Ricky Stanley
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bengtsson, Marcus
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation. Volvo CE, Bolindervägen 100, 635 10, Eskilstuna, Sweden.
    Rehman, Atiq Ur
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sohlberg, Rickard
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Case Study on Ontology Development for AI Based Decision Systems in Industry2024In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2024, p. 693-706Conference paper (Refereed)
    Abstract [en]

    Ontology development plays a vital role as it provides a structured way to represent and organize knowledge. It has the potential to connect and integrate data from different sources, enabling a new class of AI-based services and systems such as decision support systems and recommender systems. However, in large manufacturing industries, the development of such ontology can be challenging. This paper presents a use case of an application ontology development based on machine breakdown work orders coming from a Computerized Maintenance Management System (CMMS). Here, the ontology is developed using a Knowledge Meta Process: Methodology for Ontology-based Knowledge Management. This ontology development methodology involves steps such as feasibility study, requirement specification, identifying relevant concepts and relationships, selecting appropriate ontology languages and tools, and evaluating the resulting ontology. Additionally, this ontology is developed using an iterative process and in close collaboration with domain experts, which can help to ensure that the resulting ontology is accurate, complete, and useful for the intended application. The developed ontology can be shared and reused across different AI systems within the organization, facilitating interoperability and collaboration between them. Overall, having a well-defined ontology is critical for enabling AI systems to effectively process and understand information.

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