| 1. |
- Bengtsson, Marcus, et al.
(author)
-
Combining Ontology and Large Language Models to Identify Recurring Machine Failures in Free-Text Fields
- 2024
-
In: Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning. - : IOS Press. - 9781643685106 - 9781643685113 ; , s. 27-38
-
Conference paper (peer-reviewed)abstract
- Companies must enhance total maintenance effectiveness to staycompetitive, focusing on both digitalization and basic maintenance procedures.Digitalization offers technologies for data-driven decision-making, but manymaintenance decisions still lack a factual basis. Prioritizing efficiency andeffectiveness require analyzing equipment history, facilitated by usingComputerized Maintenance Management Systems (CMMS). However, CMMS dataoften contains unstructured free-text, leading to manual analysis, which is resourceintensiveand reactive, focusing on short time periods and specific equipment. Twoapproaches are available to solve the issue: minimizing free-text entries or usingadvanced methods for processing them. Free-text allows detailed descriptions butmay lack completeness, while structured reporting aids automated analysis but maylimit fault description richness. As knowledge and experience are vital assets forcompanies this research uses a hybrid approach by combining Natural LanguageProcessing with domain specific ontology and Large Language Models to extractinformation from free-text entries, enabling the possibility of real-time analysis e.g.,identifying recurring failure and knowledge sharing across global sites.
|
|
| 2. |
- Bengtsson, Marcus, 1977-, et al.
(author)
-
Combining Ontology and Large Language Models to Identify Recurring Machine Failures in Free-Text Fields
- 2024
-
In: Advances in Transdisciplinary Engineering. - : IOS Press BV. - 9781643685106 - 9781643685113 ; , s. 27-38
-
Conference paper (peer-reviewed)abstract
- 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.
|
|
| 3. |
- D'Cruze, Ricky Stanley, et al.
(author)
-
A Case Study on Ontology Development for AI Based Decision Systems in Industry
- 2024
-
In: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 693-706
-
Conference paper (peer-reviewed)abstract
- 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.
|
|
| 4. |
- Rahman, Hamidur, et al.
(author)
-
Artificial Intelligence-Based Life Cycle Engineering in Industrial Production : A Systematic Literature Review
- 2022
-
In: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 10, s. 133001-133015
-
Research review (peer-reviewed)abstract
- For the last few years, cases of applying articial intelligence (AI) to engineering activitiestowards sustainability have been reported. Life Cycle Engineering (LCE) provides a potential to systematicallyreach higher and productivity levels, owing to its holistic perspective and consideration of economic andenvironmental targets. To address the current gap to more systematic deployment of AI with LCE (AI-LCE)we have performed a systematic literature review emphasizing the three aspects:(1) the most prevalent AItechniques, (2) the current AI-improved LCE subelds and (3) the subelds with highly enhanced by AI.A specic set of inclusion and exclusion criteria were used to identify and select academic papers fromseveral elds, i.e. production, logistics, marketing and supply chain and after the selection process describedin the paper we ended up with 42 scientic papers. The study and analysis show that there are manyAI-LCE papers addressing Sustainable Development Goals mainly addressing: Industry, Innovation, andInfrastructure; Sustainable Cities and Communities; and Responsible Consumption and Production. Overall,the papers give a picture of diverse AI techniques used in LCE. Production design and Maintenance andRepair are the top explored LCE subelds whereas logistics and Procurement are the least explored subareas.Research in AI-LCE is concentrated in a few dominating countries and especially countries with a strongresearch funding and focus on Industry 4.0; Germany is standing out with numbers of publications. Thein-depth analysis of selected and relevant scientic papers are helpful in getting a more correct picture ofthe area which enables a more systematic approach to AI-LCE in the future.
|
|
| 5. |
- Rahman, Hamidur, Doctoral Student, 1984-, et al.
(author)
-
Artificial Intelligence-Based Life Cycle Engineering in Industrial Production : A Systematic Literature Review
- 2022
-
In: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 10, s. 133001-133015
-
Research review (peer-reviewed)abstract
- For the last few years, cases of applying artificial intelligence (AI) to engineering activities towards sustainability have been reported. Life Cycle Engineering (LCE) provides a potential to systematically reach higher and productivity levels, owing to its holistic perspective and consideration of economic and environmental targets. To address the current gap to more systematic deployment of AI with LCE (AI-LCE) we have performed a systematic literature review emphasizing the three aspects:(1) the most prevalent AI techniques, (2) the current AI-improved LCE subfields and (3) the subfields with highly enhanced by AI. A specific set of inclusion and exclusion criteria were used to identify and select academic papers from several fields, i.e. production, logistics, marketing and supply chain and after the selection process described in the paper we ended up with 42 scientific papers. The study and analysis show that there are many AI-LCE papers addressing Sustainable Development Goals mainly addressing: Industry, Innovation, and Infrastructure; Sustainable Cities and Communities; and Responsible Consumption and Production. Overall, the papers give a picture of diverse AI techniques used in LCE. Production design and Maintenance and Repair are the top explored LCE subfields whereas logistics and Procurement are the least explored subareas. Research in AI-LCE is concentrated in a few dominating countries and especially countries with a strong research funding and focus on Industry 4.0; Germany is standing out with numbers of publications. The in-depth analysis of selected and relevant scientific papers are helpful in getting a more correct picture of the area which enables a more systematic approach to AI-LCE in the future.
|
|
