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Campos, J., Sharma, P., Albano, M., Jantunen, E., Baglee, D. & Ferreira, L. L. (2019). Arrowhead Framework services for condition monitoring and maintenance based on the open source approach. In: 6th International Conference on Control Decision Information Technologies, 23rd -26th April, Paris. France: . Paper presented at International Conference on Control, Decision and Information Technologies, CoDIT’19, April 23-26, 2019, Paris, France. (pp. 697-702). IEEE
Open this publication in new window or tab >>Arrowhead Framework services for condition monitoring and maintenance based on the open source approach
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2019 (English)In: 6th International Conference on Control Decision Information Technologies, 23rd -26th April, Paris. France, IEEE, 2019, p. 697-702Conference paper, Published paper (Refereed)
Abstract [en]

The emergence of new Information and Communication Technologies, such as the Internet of Things and big data and data analytics provides opportunities as well as challenges for the domain of interest, and this paper discusses their importance in condition monitoring and maintenance. In addition, the Open system architecture for condition-based maintenance (OSA-CBM), and the Predictive Health Monitoring methods are gone through. Thereafter, the paper uses bearing fault data from a simulation model with the aim to produce vibration signals where different parameters of the model can be controlled. In connection to the former mentioned a prototype was developed and tested for purposes of simulated rolling element bearing fault systems signals with appropriate fault diagnostic and analytics. The prototype was developed taking into consideration recommended standards (e.g., the OSA-CBM). In addition, the authors discuss the possibilities to incorporate the developed prototype into the Arrowhead framework, which would bring possibilities to: analyze various equipment geographically dispersed, especially in this case its rolling element bearing; support servitization of Predictive Health Monitoring methods and large-scale interoperability; and, to facilitate the appearance of novel actors in the area and thus competition.

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-82322 (URN)10.1109/CoDIT.2019.8820366 (DOI)978-1-7281-0521-5 (ISBN)978-1-7281-0520-8 (ISBN)978-1-7281-0522-2 (ISBN)
Conference
International Conference on Control, Decision and Information Technologies, CoDIT’19, April 23-26, 2019, Paris, France.
Available from: 2019-04-28 Created: 2019-04-28 Last updated: 2019-10-01Bibliographically approved
Jantunen, E., Akcay, A., Campos, J., Holenderski, M., Kotkansalo, A. & Sharma, P. (2019). Business Drivers of a Collaborative, Proactive Maintenance Solution. In: Michele Albano, Erkki Jantunen, Gregor Papa, Urko Zurutuza (Ed.), The MANTIS Book: Cyber PhysicalSystem Based Proactive Collaborative Maintenance (pp. 7-35). USA: River Publishers
Open this publication in new window or tab >>Business Drivers of a Collaborative, Proactive Maintenance Solution
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2019 (English)In: The MANTIS Book: Cyber PhysicalSystem Based Proactive Collaborative Maintenance / [ed] Michele Albano, Erkki Jantunen, Gregor Papa, Urko Zurutuza, USA: River Publishers, 2019, p. 7-35Chapter in book (Refereed)
Place, publisher, year, edition, pages
USA: River Publishers, 2019
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science; Technology (byts ev till Engineering), Mechanical Engineering
Identifiers
urn:nbn:se:lnu:diva-82200 (URN)10.13052/rp-9788793609846 (DOI)978-87-93609-84-6 (ISBN)978-87-93609-85-3 (ISBN)
Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2019-05-06Bibliographically approved
Mejia Niño, C., Albano, M., Jantunen, E., Sharma, P., Campos, J. & Baglee, D. (2018). An iterative process to extract value from maintenance projects. In: Jyoti K. Sinha, Filipe Didilet (Ed.), Proceedings of the 3rd International Conference on Maintenance Engineering IncoME-III 2018: . Paper presented at 3rd International Conference on Maintenance Engineering, IncoME-III, Coimbra, Portugal, 6-7 Sep 2018 (pp. 319-335). University of Coimbra
Open this publication in new window or tab >>An iterative process to extract value from maintenance projects
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2018 (English)In: Proceedings of the 3rd International Conference on Maintenance Engineering IncoME-III 2018 / [ed] Jyoti K. Sinha, Filipe Didilet, University of Coimbra , 2018, p. 319-335Conference paper, Published paper (Refereed)
Abstract [en]

Research and development projects are producing novel maintenance strategies and techniques. Anyway, it is not straightforward to transfer results from the lab to the real world, and thus many projects, both internal to a company and in cooperation between the members of a consortium, speculate how to perform this feat, called “exploitation” in the context of European projects. This paper discusses the necessity of novel techniques in modern maintenance, and then introduces a novel approach to the problem of transferring innovation from the lab to the market. The novel approach spawns from the “spiral software development” process and proceeds as a set of iterations that bring together different stakeholders to increase the number of products, techniques and results in general that can survive the end of a research and development project. The approach was applied to a large European project, which is described as use case, and the paper reports on the encouraging results that were attained.

Place, publisher, year, edition, pages
University of Coimbra, 2018
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-82191 (URN)978-989-8200-17-4 (ISBN)
Conference
3rd International Conference on Maintenance Engineering, IncoME-III, Coimbra, Portugal, 6-7 Sep 2018
Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2019-05-07Bibliographically approved
Albano, M., Sharma, P., Campos, J. & Jantunen, E. (2018). Energy Saving by Blockchaining Maintenance.. Journal of Industrial Engineering and Management Science, 2018(1), 63-88, Article ID 4.
Open this publication in new window or tab >>Energy Saving by Blockchaining Maintenance.
2018 (English)In: Journal of Industrial Engineering and Management Science, ISSN 2446-1822, Vol. 2018, no 1, p. 63-88, article id 4Article in journal (Refereed) Published
Abstract [en]

The development and interest in Industry 4.0 together with rapid development of Cyber Physical Systems has created magnificent opportunities to develop maintenance to a totally new level. The Maintenance 4.0 vision considers massive exploitation of information regarding factories and machines to improve maintenance efficiency and efficacy, for example by facilitating logistics of spare parts, but on the other hand this creates other logistics issues on the data itself, which only exacerbate data management issues that emerge when distributed maintenance platforms scale up. In fact, factories can be delocalized with respect to the data centers, where data has to be transferred to be processed. Moreover, any transaction needs communication, be it related to purchase of spare parts, sales contract, and decisions making in general, and it has to be verified by remote parties. Keeping in mind the current average level of Overall Equipment Efficiency (50%) i.e. there is a hidden factory behind every factory, the potential is huge. It is expected that most of this potential can be realised based on the use of the above named technologies, and relying on a new approach called blockchain technology, the latter aimed at facilitating data and transactions management. Blockchain supports logistics by a distributed ledger to record transactions in a verifiable and permanent way, thus removing the need for multiple remote parties to verify and store every transaction made, in agreement with the first “r” of maintenance (reduce, repair, reuse, recycle). Keeping in mind the total industrial influence on the consumption of natural resources, such as energy, the new technology advancements can allow for dramatic savings, and can deliver important contributions to the green economy that Europe aims for. The paper introduces the novel technologies that can support sustainability of manufacturing and industry at large, and proposes an architecture to bind together said technologies to realise the vision of Maintenance 4.0.

Place, publisher, year, edition, pages
River Publishers, 2018
Keywords
OEE, Bloackchain, CPS, IoT, Maintenance
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-82164 (URN)10.13052/jiems2446-1822.2018.004 (DOI)
Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2019-05-07Bibliographically approved
Campos, J. & Askenäs, L. (2018). Important Aspects to Consider When Developing ICTs for Purposes of Fall Prevention in the eHealth Domain.. In: Nataliya Shakhovska, Sergio Montenegro, Yannick Estève, Sergey Subbotin, Natalia Kryvinska, Ivan Izonin (Ed.), Proceedings of the 1st International Workshop on Informatics & Data-Driven Medicine (IDDM 2018) Lviv, Ukraine, November 28-30, 2018.: . Paper presented at IDDM 2018 Informatics & Data-Driven Medicine, Proceedings of the 1st International Workshop on Informatics & Data-Driven Medicine (IDDM 2018), Information systems in medicine: technology and applications, pp. 229-238 (pp. 229-238). ceur-ws.org, 2255
Open this publication in new window or tab >>Important Aspects to Consider When Developing ICTs for Purposes of Fall Prevention in the eHealth Domain.
2018 (English)In: Proceedings of the 1st International Workshop on Informatics & Data-Driven Medicine (IDDM 2018) Lviv, Ukraine, November 28-30, 2018. / [ed] Nataliya Shakhovska, Sergio Montenegro, Yannick Estève, Sergey Subbotin, Natalia Kryvinska, Ivan Izonin, ceur-ws.org , 2018, Vol. 2255, p. 229-238Conference paper, Published paper (Refereed)
Abstract [en]

The current paper reviews briefly the eHealth domain, especially falldetection and prevention features, in connection with the developments of ICTs.The timely data signal providing identification of probable fall at early stages aswell as its specifics can prevent serious injuries. It is crucial for elderly peopleliving at home alone since it could affect their independent living. Therefore, thespecific and contextual characteristics of several related factors are essential tounderstand in order to be able to diminish or remove the risk of the fall of theelderly at risk. The current paper presents research in progress and its results inthe FRONT-VL project part of Celtic plus. The paper highlights essential factorsto consider when developing and implementing a semantic database model forpurposes, such as fault prevention.

Place, publisher, year, edition, pages
ceur-ws.org, 2018
Series
Ceur Workshop Proceedings, ISSN 1613-0073, E-ISSN 1613-0073 ; 2255
Keywords
fall prevention, fall detection, ehealth, elderly people, ICTs
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-82190 (URN)2-s2.0-85057772215 (Scopus ID)
Conference
IDDM 2018 Informatics & Data-Driven Medicine, Proceedings of the 1st International Workshop on Informatics & Data-Driven Medicine (IDDM 2018), Information systems in medicine: technology and applications, pp. 229-238
Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2019-06-12Bibliographically approved
Jantunen, E., Campos, J., Sharma, P. & McKay, M. (2018). Open Source Analytics Solutions for Maintenance. In: 2018 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018: . Paper presented at 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018, 10-13 April 2018, Thessaloniki, Greece (pp. 688-693). IEEE
Open this publication in new window or tab >>Open Source Analytics Solutions for Maintenance
2018 (English)In: 2018 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018, IEEE, 2018, p. 688-693Conference paper, Published paper (Refereed)
Abstract [en]

The current paper reviews existent data mining and big data analytics open source solutions. In the area of industrial maintenance engineering, the algorithms, which are part of these solutions, have started to be studied and introduced into the domain. In addition, the interest in big data and analytics have increased in several areas because of the increased amount of data produced as well as a remarkable speed attained and its variation, i.e.The so-called 3 V's (Volume, Velocity, and Variety). The companies and organizations have seen the need to optimize their decision-making processes with the support of data mining and big data analytics. The development of this kind of solutions might be a long process and for some companies something that is not within their reach for many reasons. It is, therefore, important to understand the characteristics of the open source solutions. Consequently, the authors use a framework to organize their findings. Thus, the framework used is called the knowledge discovery in databases (KDD) process for extracting useful knowledge from volumes of data. The authors suggest a modified KDD framework to be able to understand if the respective data mining/big data solutions are adequate and suitable to use in the domain of industrial maintenance engineering. © 2018 IEEE.

Place, publisher, year, edition, pages
IEEE, 2018
Series
International Conference on Control Decision and Information Technologies, ISSN 2576-3555
Keywords
big data, CBM, data mining, maintenance, open source, Decision making, Solution mining, Big Data Analytics, Big data and analytics, Data solutions, Decision making process, Knowledge discovery in database, Open sources, Open-source solutions
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science, Information Systems
Identifiers
urn:nbn:se:lnu:diva-83552 (URN)10.1109/CoDIT.2018.8394819 (DOI)000468641000115 ()2-s2.0-85050221189 (Scopus ID)9781538650653 (ISBN)
Conference
5th International Conference on Control, Decision and Information Technologies, CoDIT 2018, 10-13 April 2018, Thessaloniki, Greece
Available from: 2019-05-27 Created: 2019-05-27 Last updated: 2019-06-12Bibliographically approved
Jantunen, E., Gorostegui, U., Zurutuza, U., Albano, M., Ferreira, L. L., Hegedus, C. & Campos, J. (2018). Remote maintenance support with the aid of cyber-physical systems and cloud technology. Proceedings of the Institution of mechanical engineers. Part I, journal of systems and control engineering, 232(6), 784-794
Open this publication in new window or tab >>Remote maintenance support with the aid of cyber-physical systems and cloud technology
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2018 (English)In: Proceedings of the Institution of mechanical engineers. Part I, journal of systems and control engineering, ISSN 0959-6518, E-ISSN 2041-3041, Vol. 232, no 6, p. 784-794Article in journal (Refereed) Published
Abstract [en]

This article discusses how a business model based on traditional maintenance can evolve to generate servitization strategies, with the help of remote maintenance support. The application of cyber-physical systems and cloud technologies play a key role for such maintenance purposes. In fact, the utilization of large quantities of data collected on machines and their processing by means of advanced techniques such as machine learning enable novel techniques for condition-based maintenance. New sensor solutions that could be used in maintenance and interaction with cyber-physical systems are also presented. Here, data models are an important part of these techniques because of the huge amounts of data that are produced and should be processed. These data models have been used in a real case, supported by the Machinery Information Management Open System Alliance Open System Architecture for Condition-Based Maintenance standard architecture, for streamlining the modeling of collected data. In this context, an industrial use case is described, to enlighten the application of the presented concepts in a working pilot. Finally, current and future directions for application of cyber-physical systems and cloud technologies to maintenance are discussed.

Place, publisher, year, edition, pages
Sage Publications, 2018
Keywords
Cyber-physical systems, Internet of things, microelectromechanical systems, Machinery Information Management Open System Alliance, Open System Architecture for Condition-Based Maintenance
National Category
Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science, Information Systems
Identifiers
urn:nbn:se:lnu:diva-77402 (URN)10.1177/0959651818772939 (DOI)000439608900013 ()2-s2.0-85047389495 (Scopus ID)
Available from: 2018-08-29 Created: 2018-08-29 Last updated: 2019-08-29Bibliographically approved
Campos, J., Sharma, P., Gabiria, U. G., Jantunen, E. & Baglee, D. (2017). A big data analytical architecture for the Asset Management. In: McAloone, TC Pigosso, DCA Mortensen, NH Shimomura, Y (Ed.), Industrial Product/Service-Systems (IPSS) Conference: Circular Perspectives on Product/Service-Systems. Paper presented at 9th CIRP Industrial Product/Service-Systems (IPSS) Conference - Circular Perspectives on Product/Service-Systems, JUN 19-21, 2017, Copenhagen, DENMARK (pp. 369-374). Elsevier
Open this publication in new window or tab >>A big data analytical architecture for the Asset Management
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2017 (English)In: Industrial Product/Service-Systems (IPSS) Conference: Circular Perspectives on Product/Service-Systems / [ed] McAloone, TC Pigosso, DCA Mortensen, NH Shimomura, Y, Elsevier, 2017, p. 369-374Conference paper, Published paper (Refereed)
Abstract [en]

The paper highlights the characteristics of data and big data analytics in manufacturing, more specifically for the industrial asset management. The authors highlight important aspects of the analytical system architecture for purposes of asset management. The authors cover the data and big data technology aspects of the domain of interest. This is followed by application of the big data analytics and technologies, such as machine learning and data mining for asset management. The paper also presents the aspects of visualisation of the results of data analytics. In conclusion, the architecture provides a holistic view of the aspects and requirements of a big data technology application system for purposes of asset management. The issues addressed in the paper, namely equipment health, reliability, effects of unplanned breakdown, etc., are extremely important for today's manufacturing companies. Moreover, the customer's opinion and preferences of the product/services are crucial as it gives an insight into the ways to improve in order to stay competitive in the market. Finally, a successful asset management function plays an important role in the manufacturing industry, which is dependent on the support of proper ICTs for its further success. (C) 2017 The Authors Published by Elsevier B.V.

Place, publisher, year, edition, pages
Elsevier, 2017
Series
Procedia CIRP, ISSN 2212-8271 ; 64
Keywords
Asset Management, Big data, Big data analytics, Data mining
National Category
Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science, Information Systems
Identifiers
urn:nbn:se:lnu:diva-69674 (URN)10.1016/j.procir.2017.03.019 (DOI)000414528200063 ()2-s2.0-85021805273 (Scopus ID)
Conference
9th CIRP Industrial Product/Service-Systems (IPSS) Conference - Circular Perspectives on Product/Service-Systems, JUN 19-21, 2017, Copenhagen, DENMARK
Available from: 2018-01-10 Created: 2018-01-10 Last updated: 2019-08-29Bibliographically approved
Sharma, P., Baglee, D., Campos, J. & Jantunen, E. (2017). Big data collection and analysis for manufacturing organisations. Big Data and Information Analytics, 2(2), 127-139
Open this publication in new window or tab >>Big data collection and analysis for manufacturing organisations
2017 (English)In: Big Data and Information Analytics, ISSN 2380-6966, Vol. 2, no 2, p. 127-139Article in journal (Refereed) Published
Abstract [en]

Data mining applications are becoming increasingly important for the wide range of manufacturing and maintenance processes. During daily operations, large amounts of data are generated. This large volume and variety of data, arriving at a greater velocity has its own advantages and disadvantages. On the negative side, the abundance of data often impedes the ability to extract useful knowledge. In addition, the large amounts of data stored in often unconnected databases make it impractical to manually analyse for valuable decision-making information. However, an advent of new generation big data analytical tools has started to provide large scale benefits for the organizations. The paper examines the possible data inputs from machines, people and organizations that can be analysed for maintenance. Further, the role of big data within maintenance is explained and how, if not managed correctly, big data can create problems rather than provide solutions. The paper highlights the need to have advanced mining techniques to enable conversion of data into information in an acceptable time frame and to have modern analytical tools to extract value from the big datasets.

Place, publisher, year, edition, pages
American Institute of Mathematical Sciences, 2017
Keywords
Big data, CBM, manufacturing
National Category
Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-68769 (URN)10.3934/bdia.2017002 (DOI)
Available from: 2017-11-16 Created: 2017-11-16 Last updated: 2018-01-13Bibliographically approved
Campos, J., Sharma, P., Jantunen, E., Baglee, D. & Fumagalli, L. (2017). Business Performance Measurements in Asset Management with the Support of Big Data Technologies. Management Systems in Production Engineering, 25(3), 143-149
Open this publication in new window or tab >>Business Performance Measurements in Asset Management with the Support of Big Data Technologies
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2017 (English)In: Management Systems in Production Engineering, ISSN 2299-0461, Vol. 25, no 3, p. 143-149Article in journal (Refereed) Published
Abstract [en]

The paper reviews the performance measurement in the domain of interest. Important data in asset management are further, discussed. The importance and the characteristics of today’s ICTs capabilities are also mentioned in the paper. The role of new concepts such as big data and data mining analytical technologies in managing the performance measurements in asset management are discussed in detail. The authors consequently suggest the use of the modified Balanced Scorecard methodology highlighting both quantitative and qualitative aspects, which is crucial for optimal use of the big data approach and technologies.

Place, publisher, year, edition, pages
De Gruyter Open, 2017
Keywords
business performance measurements, asset management, big data technologies
National Category
Communication Systems Computer Systems
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-68666 (URN)10.1515/mspe-2017-0021 (DOI)000408931800001 ()
Available from: 2017-11-09 Created: 2017-11-09 Last updated: 2018-12-04Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7048-8089

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