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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
Salonen, A. (2024). On the Need for Human Centric 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. 465-475). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>On the Need for Human Centric Maintenance Technologies
2024 (English)In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2024, p. 465-475Conference paper, Published paper (Refereed)
Abstract [en]

The digitalization of manufacturing industry, known as e.g., Industry 4.0 or smart production, has opened new opportunities for real-time optimization of production systems. Also, this technological leap has provided new possibilities for the maintenance of production equipment to become data driven and in many cases predictive. This fourth industrial revolution is changing the role of humans at the shop floor. Visions of the dark factory arises, meaning fully automated factories where humans are redundant, both for physical processing and for decision making. The research on Smart maintenance shows great advances in predictive diagnostics and prognostic techniques. However, in manufacturing industry, studies have shown that up to 50–60% of equipment breakdowns are due to human errors. Some of these errors are partly addressed through the development of improved information aid, such as e.g., instructions through Augmented Reality and training in Virtual Reality. Still, the root cause of human errors in manufacturing industry haven’t been properly categorized in terms of e.g., neglect, lack of competence, unclear processes, or poor leadership. In this paper the potential of data driven maintenance is discussed from a human centric perspective. Considering the large part of failures being due to human factors and the possibilities of improvement through implementation of smart technologies, this paper argues for exploring the root causes of human errors in discrete item manufacturing systems and address the proper human centric technologies as a means of reducing these failures.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2024
Keywords
Human centric, Human factors, Smart maintenance, Augmented reality, Decision making, Errors, Human engineering, Real time systems, Virtual reality, Data driven, Human errors, Human-centric, Maintenance technologies, Manufacturing industries, Production equipments, Production system, Realtime optimizations (RTO), Root cause, Maintenance
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:mdh:diva-65364 (URN)10.1007/978-3-031-39619-9_34 (DOI)2-s2.0-85181977934 (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
Salonen, A. (2023). What is Smart Maintenance in Manufacturing Industry?. In: Lecture Notes in Mechanical Engineering: . Paper presented at 6th World Congress on Engineering Asset Management, WCEAM 2022Seville5 October 2022 through 7 October 2022 Code 291789 (pp. 366-374). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>What is Smart Maintenance in Manufacturing Industry?
2023 (English)In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2023, p. 366-374Conference paper, Published paper (Refereed)
Abstract [en]

The ongoing transformation of manufacturing industry into digitalized production, Industry 4.0, has put new perspectives on the maintenance of production systems. The technologies offer an array of new possibilities in optimization of maintenance and data driven decision making. On the other hand, these new technologies offer a lot of challenges in form of investment costs, need for new competences, and how to handle the equipment legacy, i.e. upgrading old equipment. Many researchers associate data driven decision making with intelligent sensors, cloud computing and cyber physical systems, but are these technologies the most cost-effective way of achieving data driven maintenance? The aim of this paper is to discuss how manufacturing industry should approach smart maintenance in order to improve the industry’s competitiveness, rather than spending money on technology that doesn’t contribute. The basis for the discussion will mainly be a literature study but additional empirical data may be included.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
Keywords
Competition, Cost effectiveness, Embedded systems, Maintenance, Cloud-computing, Data driven decision, Decisions makings, Intelligent sensors, Investment costs, Manufacturing industries, Old equipment, Optimisations, Production industries, Production system, Decision making
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:mdh:diva-62210 (URN)10.1007/978-3-031-25448-2_35 (DOI)2-s2.0-85151145822 (Scopus ID)9783031254475 (ISBN)
Conference
6th World Congress on Engineering Asset Management, WCEAM 2022Seville5 October 2022 through 7 October 2022 Code 291789
Available from: 2023-04-12 Created: 2023-04-12 Last updated: 2023-04-12Bibliographically 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
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
Salonen, A. & Gopalakrishnan, M. (2021). Practices of preventive maintenance planningin discrete manufacturing industry. Journal of Quality in Maintenance Engineering, 27(2), 331-350
Open this publication in new window or tab >>Practices of preventive maintenance planningin discrete manufacturing industry
2021 (English)In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 27, no 2, p. 331-350Article in journal (Refereed) Published
Abstract [en]

"Purpose – The purpose of this study was to assess the readiness of the Swedish manufacturing industry to implement dynamic, data-driven preventive maintenance (PM) by identifying the gap between the state of the art and the state of practice.

Design/methodology/approach – An embedded multiple case study was performed in which some of the largest companies in the discrete manufacturing industry, that is, mechanical engineering, were surveyed regarding the design of their PM programmes.

Findings – The studied manufacturing companies make limited use of the existing scientific state of the art when designing their PM programmes. They seem to be aware of the possibilities for improvement, but they also see obstacles to changing their practices according to future requirements.

Practical implications – The results of this study will benefit both industry professionals and academicians, setting the initial stage for the development of data-driven, diversified and dynamic PM programmes.

Originality/Value – First and foremost, this study maps the current state and practice in PM planning among some of the larger automotive manufacturing industries in Sweden. This work reveals a gap between the stateof the art and the state of practice in the design of PM programmes. Insights regarding this gap show large improvement potentials which may prove important for academics as well as practitioners."

Keywords
Preventive maintenance optimisation, Dynamic maintenance, Diversified preventive maintenance, Manufacturing industry
National Category
Reliability and Maintenance
Research subject
Innovation and Design
Identifiers
urn:nbn:se:mdh:diva-52197 (URN)10.1108/JQME-04-2019-0041 (DOI)000645194300005 ()2-s2.0-85088790062 (Scopus ID)
Funder
Vinnova, 2015-06887
Available from: 2020-10-30 Created: 2020-10-30 Last updated: 2021-06-21Bibliographically approved
Campos, J., Kans, M. & Salonen, A. (2020). A Project Management Methodology for the Digitalisation of the Industrial Maintenance Domain. In: Smart Innov. Syst. Technol.: . Paper presented at 3 September 2019 through 5 September 2019 (pp. 621-629). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>A Project Management Methodology for the Digitalisation of the Industrial Maintenance Domain
2020 (English)In: Smart Innov. Syst. Technol., Springer Science and Business Media Deutschland GmbH , 2020, p. 621-629Conference paper, Published paper (Refereed)
Abstract [en]

Efficient management of Information and Communication Technology (ICT) projects is a crucial factor since on many occasions they fail to meet the expected demands and requirements of the different stakeholders. It is well known that poor project management, like inadequate risk management and inadequate project planning, is one of the main reasons for ICT project failure. In addition, with the emergence of the new ICTs such as the Internet of Things, cloud computing, and big data the digitalisation of the maintenance domain has become even more complex. It is, therefore, essential to revisit standard processes when carrying out software development projects for the domain of interest. A broad selection of software development methodologies exists today. However, these are general-purpose development approaches, and it is, therefore, crucial to have an understanding of the best options and best practices of the existent methodologies for context-specific development projects. Thus, the authors present a literature review of ICT project methodologies and later discuss these findings taking into consideration the characteristics of the domain of interest. Based on this, an ICT project management methodology suitable for the Industrial maintenance domain is suggested. © 2020, Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2020
Keywords
Digitalisation, ICT project management, Industrial maintenance, Condition monitoring, Maintenance, Risk management, Software design, Development approach, Development project, Efficient managements, Information and Communication Technologies, Project management methodology, Selection of software, Software development projects, Project management
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-50994 (URN)10.1007/978-3-030-57745-2_52 (DOI)2-s2.0-85091289094 (Scopus ID)9783030577445 (ISBN)
Conference
3 September 2019 through 5 September 2019
Note

Conference code: 244529; Export Date: 1 October 2020; Conference Paper; Correspondence Address: Campos, J.; Linnaeus UniversitySweden; email: jaime.campos@lnu.se

Available from: 2020-10-01 Created: 2020-10-01 Last updated: 2020-10-01Bibliographically 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/0000-0002-7494-1474

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