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D'Cruze, R. S., Ahmed, M. U., Bengtsson, M., Rehman, A. U., Funk, P. & Sohlberg, R. (2024). A Case Study on Ontology Development for AI Based Decision Systems in Industry. In: Lecture Notes in Mechanical Engineering: . Paper presented at 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023 (pp. 693-706). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>A Case Study on Ontology Development for AI Based Decision Systems in Industry
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2024 (English)In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2024, p. 693-706Conference paper, Published 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.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2024
Keywords
Custom NER, Industrial AI, Machine failures prediction, Ontology development, Artificial intelligence, Decision support systems, Interoperability, Iterative methods, Knowledge management, AI systems, Case-studies, Decision systems, Failures prediction, Machine failure, Machine failure prediction, Ontology's, Ontology
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-65369 (URN)10.1007/978-3-031-39619-9_51 (DOI)2-s2.0-85181980940 (Scopus ID)9783031396182 (ISBN)
Conference
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023
Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-01-17Bibliographically approved
Giliyana, S., Karlsson, J., Bengtsson, M., Salonen, A., Adoue, V. & Hedelind, M. (2024). A Testbed for Smart Maintenance Technologies. In: Lecture Notes in Mechanical Engineering: . Paper presented at 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023 (pp. 437-450). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>A Testbed for Smart Maintenance Technologies
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2024 (English)In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2024, p. 437-450Conference paper, Published paper (Refereed)
Abstract [en]

Industry 4.0 presents nine technologies including Industrial Internet of Things (IIoT), Big Data and Analytics, Augmented Reality (AR), etc. Some of the technologies play an important role in the development of smart maintenance technologies. Previous research presents several technologies for smart maintenance. However, one problem is that the manufacturing industry still finds it challenging to implement smart maintenance technologies in a value-adding way. Open questionnaires and interviews have been used to collect information about the current needs of the manufacturing industry. Both the empirical findings of this paper, as well as previous research, show that knowledge is the most common challenge when implementing new technologies. Therefore, in this paper, we develop and present a testbed for how to approach smart maintenance technologies and to share technical knowledge to the manufacturing industry.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2024
Keywords
Knowledge, Smart maintenance technologies, Testbed, Augmented reality, Industry 4.0, Maintenance, 'current, Empirical findings, Maintenance technologies, Manufacturing industries, Smart maintenance technology, Testbeds
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:mdh:diva-65365 (URN)10.1007/978-3-031-39619-9_32 (DOI)2-s2.0-85181978943 (Scopus ID)9783031396182 (ISBN)
Conference
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023
Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-01-17Bibliographically approved
Bengtsson, M., Pettersson, R., Giliyana, S. & Salonen, A. (2024). The Importance of Using Domain Knowledge When Designing and Implementing Data-Driven Decision Models for Maintenance: Insights from Industrial Cases. In: Lecture Notes in Mechanical Engineering: . Paper presented at 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023 (pp. 601-614). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>The Importance of Using Domain Knowledge When Designing and Implementing Data-Driven Decision Models for Maintenance: Insights from Industrial Cases
2024 (English)In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2024, p. 601-614Conference paper, Published paper (Refereed)
Abstract [en]

The advanced technologies available in the development of Smart Maintenance within Industry 4.0 have the potential to significantly improve the efficiency of industrial maintenance. However, it is important to be careful when deciding which technologies to implement for a given application and when evaluating the quality of the data generated. Otherwise, what should be cost-effective solutions may end up being cost-driving. The use of domain knowledge in selecting, developing, implementing, setting up, and utilizing these technologies is increasingly important for achieving success. In this paper, we will elaborate on this topic by presenting and analyzing insights from industrial cases, drawing on the authors’ extensive experience in the field.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2024
Keywords
Data-driven decisions, Domain knowledge, Industrial cases, Maintenance, Smart maintenance technologies, Cost effectiveness, Advanced technology, Cost-effective solutions, Data driven decision, Decision modeling, Industrial case, Industrial maintenance, IS costs, Maintenance technologies, Smart maintenance technology
National Category
Reliability and Maintenance
Identifiers
urn:nbn:se:mdh:diva-65367 (URN)10.1007/978-3-031-39619-9_44 (DOI)2-s2.0-85181979974 (Scopus ID)9783031396182 (ISBN)
Conference
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023
Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-01-17Bibliographically approved
Giliyana, S., Bengtsson, M. & Salonen, A. (2023). Perspectives on Smart Maintenance Technologies – A Case Study in Small and Medium-Sized Enterprises (SMEs) Within Manufacturing Industry. In: 16th WCEAM Proceedings: . Paper presented at 16th World Congress on Engineering Asset Management (WCEAM), Seville from 5–7 October 2022. (pp. 571-585). Springer Nature
Open this publication in new window or tab >>Perspectives on Smart Maintenance Technologies – A Case Study in Small and Medium-Sized Enterprises (SMEs) Within Manufacturing Industry
2023 (English)In: 16th WCEAM Proceedings, Springer Nature , 2023, p. 571-585Conference paper, Published paper (Refereed)
Abstract [en]

Industry 4.0 consists of nine technological pillars: IIoT, Cloud Computing, Big Data and Analytics, AR, etc. Some of the pillars play an essential role in maintenance development. Previous research presents many technologies for smart maintenance, but one prevailing problem is that there are still challenges to implementing smart maintenance technologies cost-effectively in the manufacturing industry. Therefore, we explore perspectives on smart maintenance technologies from respondents within 15 manufacturing SMEs. We start by investigating whether the companies had implemented smart maintenance technologies, if so, in what context. Then, we explore perspectives from the manufacturing SMEs on added values, challenges, opportunities, advantages, and disadvantages of smart maintenance technologies. However, as none of the case companies had implemented any Smart Maintenance Technologies, only implementation challenges could be investigated.

Place, publisher, year, edition, pages
Springer Nature, 2023
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356, E-ISSN 2195-4364
National Category
Engineering and Technology Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:mdh:diva-62070 (URN)10.1007/978-3-031-25448-2_53 (DOI)2-s2.0-85151146920 (Scopus ID)978-3-031-25448-2 (ISBN)
Conference
16th World Congress on Engineering Asset Management (WCEAM), Seville from 5–7 October 2022.
Available from: 2023-03-14 Created: 2023-03-14 Last updated: 2023-11-13Bibliographically approved
Ahmed, M. U., Bengtsson, M., Salonen, A. & Funk, P. (2022). Analysis of Breakdown Reports Using Natural Language Processing and Machine Learning. In: Lecture Notes in Mechanical Engineering: . Paper presented at International Congress and Workshop on Industrial AI, IAI 2021 Virtual, Online 6 October 2021 through 7 October 2021 Code 272219 (pp. 40-52). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Analysis of Breakdown Reports Using Natural Language Processing and Machine Learning
2022 (English)In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2022, p. 40-52Conference paper, Published paper (Refereed)
Abstract [en]

Proactive maintenance management of world-class standard is close to impossible without the support of a computerized management system. In order to reduce failures, and failure recurrence, the key information to log are failure causes. However, Computerized Maintenance Management System (CMMS) seems to be scarcely used for analysis for improvement initiatives. One part of this is due to the fact that many CMMS utilizes free-text fields which may be difficult to analyze statistically. The aim of this study is to apply Natural Language Processing (NPL), Ontology and Machine Learning (ML) as a means to analyze free-textual information from a CMMS. Through the initial steps of the study, it was concluded though that none of these methods were able to find any suitable hidden patterns with high-performance accuracy that could be related to recurring failures and their root causes. The main reason behind that was that the free-textual information was too unstructured, in terms of for instance: spelling- and grammar mistakes and use of slang. That is the quality of the data are not suitable for the analysis. However, several improvement potentials in reporting and to develop the CMMS further could be provided to the company so that they in the future more easily will be able to analyze its maintenance data.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2022
Keywords
Computerized maintenance management system, Machine learning, Natural language processing, Recurring breakdowns, Root cause failure analysis
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-57576 (URN)10.1007/978-3-030-93639-6_4 (DOI)000777604600004 ()2-s2.0-85125258793 (Scopus ID)9783030936389 (ISBN)
Conference
International Congress and Workshop on Industrial AI, IAI 2021 Virtual, Online 6 October 2021 through 7 October 2021 Code 272219
Available from: 2022-03-09 Created: 2022-03-09 Last updated: 2022-06-07Bibliographically approved
Bengtsson, M., Andersson, L.-G. & Ekstrom, P. (2022). Measuring preconceived beliefs on the results of overall equipment effectiveness - A case study in the automotive manufacturing industry. Journal of Quality in Maintenance Engineering, 28(2), 391-410
Open this publication in new window or tab >>Measuring preconceived beliefs on the results of overall equipment effectiveness - A case study in the automotive manufacturing industry
2022 (English)In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 28, no 2, p. 391-410Article in journal (Refereed) Published
Abstract [en]

Purpose The purpose of the study is to test if it, by the use of a survey methodology, is possible to measure managers' awareness on, and specifically if there exist preconceived beliefs on, overall equipment effectiveness (OEE) results. The paper presents the design of the survey methodology as well as a test of the survey in one case company. Design/methodology/approach Actual OEE logs from a case company are collected and a survey on the data is designed and managers at the same case company are asked to answer the survey. The survey results are followed-up by an interview study in order to get deeper insights to both the results of the survey as well as the OEE strategy at the case company. Findings The findings show that the managers at this particular case company, on a general level, does not suffer too much from preconceived beliefs. However, it is clear that the managers have a preconceived belief that lack of material is logged as a loss much more often than what it actually is. Research limitations/implications The test has only been performed with data from one case company within the automotive manufacturing industry and only the managers at that case company has been active in the test. Practical implications The survey methodology can be replicated and used by other companies to find out how aware their employees are on their OEE results and if possible preconceived beliefs exists. Originality/value To the authors' knowledge, this is the first attempt at measuring if preconceived beliefs on OEE results exist.

Place, publisher, year, edition, pages
EMERALD GROUP PUBLISHING LTD, 2022
Keywords
Overall equipment effectiveness, Lean manufacturing, Manufacturing performance improvement
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:mdh:diva-53474 (URN)10.1108/JQME-03-2020-0016 (DOI)000610573100001 ()2-s2.0-85099694640 (Scopus ID)
Available from: 2021-02-19 Created: 2021-02-19 Last updated: 2022-08-29Bibliographically approved
Giliyana, S., Salonen, A. & Bengtsson, M. (2022). Perspectives on Smart Maintenance Technologies – A Case Study in Large Manufacturing Companies. In: Amos H.C. Ng, Anna Syberfeldt, Dan Högberg, Magnus Holm (Ed.), Advances in Transdisciplinary Engineering: . Paper presented at SPS2022, Proceedings of the 10th Swedish Production Symposium, Skövde, Sweden, 26-29 April 2022 (pp. 255-266). IOS Press, 21
Open this publication in new window or tab >>Perspectives on Smart Maintenance Technologies – A Case Study in Large Manufacturing Companies
2022 (English)In: Advances in Transdisciplinary Engineering / [ed] Amos H.C. Ng, Anna Syberfeldt, Dan Högberg, Magnus Holm, IOS Press, 2022, Vol. 21, p. 255-266Conference paper, Published paper (Refereed)
Abstract [en]

The manufacturing industry faces significant technical challenges due to the industry 4.0 technologies, which play an essential role in maintenance development. Maintenance in industry 4.0, also named smart maintenance, maintenance 4.0, predictive maintenance, etc., is boosted using industry 4.0 technologies, such as Industrial Internet of Things (IIoT), Big Data and Analytics, Cloud Computing, Augmented Reality (AR), Additive Manufacturing (AM), etc. Previous research presents several smart maintenance technologies, but the manufacturing industry still finds it challenging to implement the technologies cost-effectively. One problem is that there is insufficient research on how smart maintenance technologies can be implemented cost-effectively and add value to the manufacturing industry. Therefore, this paper aims to explore perspectives on smart maintenance technologies: 1) if there are any implemented smart maintenance technologies, 2) in what context, 3) added values, 4) challenges, 5) opportunities, 6) advantages, and 7) disadvantages with the technologies. This paper presents the results of a case study based on an online open questionnaire with respondents working in maintenance organizations in large manufacturing companies. 

Place, publisher, year, edition, pages
IOS Press, 2022
Keywords
Smart Maintenance, Maintenance 4.0, Predictive Maintenance, Industry 4.0
National Category
Engineering and Technology
Research subject
Innovation and Design
Identifiers
urn:nbn:se:mdh:diva-58298 (URN)10.3233/ATDE220145 (DOI)2-s2.0-85132808966 (Scopus ID)978-1-64368-268-6 (ISBN)978-1-64368-269-3 (ISBN)
Conference
SPS2022, Proceedings of the 10th Swedish Production Symposium, Skövde, Sweden, 26-29 April 2022
Available from: 2022-05-25 Created: 2022-05-25 Last updated: 2023-11-13Bibliographically approved
Bengtsson, M., Alm, P. & Tjulin, B. (2022). Visualizing the Effects of Chronic Versus Sporadic Losses in Manufacturing Industries: A Case Study. In: Advances in Transdisciplinary Engineering: . Paper presented at 10th Swedish Production Symposium, SPS 2022, Skovde (pp. 3-14). IOS Press BV
Open this publication in new window or tab >>Visualizing the Effects of Chronic Versus Sporadic Losses in Manufacturing Industries: A Case Study
2022 (English)In: Advances in Transdisciplinary Engineering, IOS Press BV , 2022, p. 3-14Conference paper, Published paper (Refereed)
Abstract [en]

Measuring overall equipment effectiveness can be rather difficult. Particularly to capture all chronic losses, those losses that occur frequently, often on a daily basis, and often with a rather quick and easy fix without involvement of other support functions. Sporadic losses, on the other hand, such as breakdowns, lack of material or manpower is quite easily logged as it gets noticed. This issue is clearly a bigger one when discussing manual or semi-automatic OEE measurement systems. As a complement to this and as a way of visualizing effects of chronic versus sporadic losses a tool has been developed and tested in a case study in an industrial setting. 

Place, publisher, year, edition, pages
IOS Press BV, 2022
Keywords
breakdowns, Chronic losses, minor stoppages, overall equipment effectiveness, sporadic losses
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:mdh:diva-59540 (URN)10.3233/ATDE220121 (DOI)2-s2.0-85132794741 (Scopus ID)9781614994398 (ISBN)
Conference
10th Swedish Production Symposium, SPS 2022, Skovde
Available from: 2022-07-06 Created: 2022-07-06 Last updated: 2022-07-06Bibliographically approved
Bengtsson, M., Andersson, L.-G. -. & Ekström, P. (2020). Misconceptions within the Use of Overall Equipment Effectiveness - A Theoretical Discussion on Industrial Examples. In: Advances in Transdisciplinary Engineering, Volume 13: . Paper presented at 9th Swedish Production Symposium, SPS 2020; Virtual, Online; Sweden; 7 October 2020 through 8 October 2020 (pp. 36-47). IOS Press BV
Open this publication in new window or tab >>Misconceptions within the Use of Overall Equipment Effectiveness - A Theoretical Discussion on Industrial Examples
2020 (English)In: Advances in Transdisciplinary Engineering, Volume 13, IOS Press BV , 2020, p. 36-47Conference paper, Published paper (Refereed)
Abstract [en]

Overall equipment effectiveness (OEE) is a common performance measure used in manufacturing industry to identify and prioritize losses to perform improvement work on in order to increase the effectiveness of equipment. There exist challenges though, both in implementing OEE as well as in running an OEE-program. Some of these challenges include lack of training and awareness, lack of focus, risk of misunderstanding the measure etc. This paper will deal with some of the possible misconceptions within the use of OEE that might arise during implementation or in continuously running an OEE-program. Some of the topics of misconceptions that will be discussed include: no financial issues are taken into consideration; that the factors of availability; performance and quality are not weighted; the connection to productivity is not always clear; the importance of cross-functionality of the measurement and work method; the issue of comparison of OEE results; and last but not least the view on and hunt for world class levels. The paper will discuss these (and some additional ones) theoretically and suggest some counter-Actions so that they may be avoided. 

Place, publisher, year, edition, pages
IOS Press BV, 2020
Keywords
Engineering, Industrial engineering, Manufacturing industries, Overall equipment effectiveness, Performance measure, World class, Risk assessment
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:mdh:diva-53017 (URN)10.3233/ATDE200141 (DOI)2-s2.0-85098646424 (Scopus ID)9781614994398 (ISBN)
Conference
9th Swedish Production Symposium, SPS 2020; Virtual, Online; Sweden; 7 October 2020 through 8 October 2020
Available from: 2021-01-14 Created: 2021-01-14 Last updated: 2021-01-14Bibliographically approved
Salonen, A., Bengtsson, M. & Fridholm, V. (2020). The Possibilities of Improving Maintenance through CMMS Data Analysis. In: Advances in Transdisciplinary Engineering, Volume 13: . Paper presented at 9th Swedish Production Symposium, SPS 2020; Virtual, Online; Sweden; 7 October 2020 through 8 October 2020 (pp. 249-260). IOS Press BV
Open this publication in new window or tab >>The Possibilities of Improving Maintenance through CMMS Data Analysis
2020 (English)In: Advances in Transdisciplinary Engineering, Volume 13, IOS Press BV , 2020, p. 249-260Conference paper, Published paper (Refereed)
Abstract [en]

Maintenance of production equipment is one of the most critical support actions in manufacturing companies for staying competitive. More recently, with the introduction of Industry 4.0, academia, as well as industry, put a lot of effort into condition monitoring in order to implement predictive maintenance. Most stakeholders agree that maintenance need to be more data-driven. However, in order to draw true advantage of data-driven decisions, it is necessary for manufacturing companies to have implemented basic maintenance to a high standard in order to reduce for example: recurring failures, human errors, unsafe machines, etc. The real-Time data can then be used to improve efficiency of maintenance tasks and schedule that adds value to the processes. In manufacturing industry, maintenance actions are commonly administered in a Computerized Maintenance Management System, CMMS, still, rather few companies analyze their maintenance records. Behind these data there is often a treasure of improvement opportunities that could be used to improve basic maintenance. The purpose of this paper is to explore how historical data from a CMMS can be used in order to improve maintenance effectiveness and efficiency of activities. In order to exemplify the possibilities of analyzing CMMS records, a case study has been performed in a plant, manufacturing driveline components for heavy construction vehicles. The study shows that one major obstacle for utilizing the CMMS data is poor description of faults and failures when it comes to work order requests, mostly performed by operators and assemblers, as well as work order reporting, mostly performed by repairmen and maintenance technicians. However, by thorough analysis of well described corrective maintenance, it is possible for industry to understand the nature of the occurring breakdowns and thus, refine the preventive maintenance program in order to further increase the dependability of the production system. .

Place, publisher, year, edition, pages
IOS Press BV, 2020
Keywords
Condition monitoring, Efficiency, Manufacture, Computerized maintenance management system, Corrective maintenance, Data driven decision, Effectiveness and efficiencies, Maintenance records, Manufacturing companies, Manufacturing industries, Production equipments, Maintenance
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:mdh:diva-53016 (URN)10.3233/ATDE200163 (DOI)2-s2.0-85098625065 (Scopus ID)9781614994398 (ISBN)
Conference
9th Swedish Production Symposium, SPS 2020; Virtual, Online; Sweden; 7 October 2020 through 8 October 2020
Available from: 2021-01-14 Created: 2021-01-14 Last updated: 2021-01-14Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0729-0122

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