Moving towards data-driven decision-making in maintenance

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorKubler, Sylvain, Dr., Université de Lorraine, France
dc.contributor.authorMadhikermi, Manik
dc.contributor.departmentTietotekniikan laitosfi
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.labAdaptive Systems of Intelligent Agentsen
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.schoolSchool of Scienceen
dc.contributor.supervisorFrämling, Kary, Prof., Aalto University, Department of Computer Science, Finland
dc.date.accessioned2019-05-23T09:01:15Z
dc.date.available2019-05-23T09:01:15Z
dc.date.defence2019-06-10
dc.date.issued2019
dc.description.abstractTraditionally, in the maintenance industry, maintenance efficiency is limited by the capability of the experts making the decision. However, the advancement of digital technologies made it possible to improve the effectiveness and efficiency of maintenance activities by adding insight from the data to expert assessment. The opportunity provided by data for decision making made the companies to shift towards a new type of maintenance strategy called data-driven maintenance. Despite of opportunities, data and analytical tools' companies are still struggling to fully harness data asset to improve maintenance activities because of data-centric challenges. Hence, the main objective of this dissertation is to identify and mitigate those challenges that limit organizational decision-making capabilities to improve maintenance effectiveness. In this dissertation, firstly, quantitative and descriptive analyses of case studies in Finnish Multinational Manufacturing Companies have been carried out to identify key data-centric challenges. The study identified Data Quality, Interoperability, and Data extraction as key challenges. Furthermore, each of the identified challenges have been investigated through one or more original publications. The main results achieved in this dissertation are methods and frameworks to i) assess and compare data quality of maintenance  reporting procedure ii) two-level interoperability framework for inter-system interoperability iii) data discovery methodology to extract data for Extract, Transform and Load process. The applicability and validity of each of the proposed methodologies and framework has been validated through one or multiple use cases. For validation, three different tools namely, MRQA Dashboard, Open-messaging Middleware, and Data Model Logger have been developed to tackle each of the identified data-centric challenges.en
dc.format.extent105 + app. 77
dc.format.mimetypeapplication/pdfen
dc.identifier.isbn978-952-60-8582-1 (electronic)
dc.identifier.isbn978-952-60-8581-4 (printed)
dc.identifier.issn1799-4942 (electronic)
dc.identifier.issn1799-4934 (printed)
dc.identifier.issn1799-4934 (ISSN-L)
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/38096
dc.identifier.urnURN:ISBN:978-952-60-8582-1
dc.language.isoenen
dc.opnCouto Barone, Dante Augusto, Prof., Federal University of Rio Grande do Sul Porto Alegre, Brazil
dc.opnLamouri, Samir, Prof., ENSAM, Arts et métiers ParisTech., France
dc.publisherAalto Universityen
dc.publisherAalto-yliopistofi
dc.relation.haspart[Publication 1]: Manik Madhikermi, Andrea Buda, Bhargav Dave, Kary Främling. Key Data Quality Pitfalls for Condition Based Maintenance. In 2017 2nd International Conference on System Reliability and Safety (ICSRS), Milan, Italy, pp. 474-480, Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201808214639. DOI: 10.1109/ICSRS.2017.8272868
dc.relation.haspart[Publication 2]: Manik Madhikermi, Sylvain Kubler, Jérémy Robert, Andrea Buda, Kary Främling. Data quality assessment of maintenance reporting procedures. Expert Systems with Applications, 2016, Vol. 63, pp. 145-164, DOI: 10.1016/j.eswa.2016.06.043
dc.relation.haspart[Publication 3]: Sylvain Kubler, Manik Madhikermi, Andrea Buda, Kary Främling, William Derigent, André Thomas. Towards data exchange interoperability in building lifecycle management. In 2014 IEEE Emerging Technology and Factory Automation conference, Barcelona, Spain, pp. 1-8, DOI: 10.1109/ETFA.2014.7005093
dc.relation.haspart[Publication 4]: Manik Madhikermi, Narges Yousefnezhad, Kary Främling. Data Exchange Standard for Industrial Internet of Things. In 2018 3rd International Conference on System Reliability and Safety, Barcelona, Spain, pp. 53-61, DOI: 10.1109/ICSRS.2018.8688847
dc.relation.haspart[Publication 5]: Manik Madhikermi, Narges Yousefnezhad, Kary Främling. Heat Recovery Unit Failure Detection in Air Handling Unit. In IFIP International Conference on Advances in Production Management Systems, Seoul, Korea, pp. 343-350, August 2018. Full text available in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201810165343. DOI: 10.1007/978-3-319-99707-0_43
dc.relation.haspart[Publication 6]: Manik Madhikermi, Narges Yousefnezhad, Kary Främling. Data discovery method for Extract-Transform-Load. 2019 5th International Conference on Information Management and Industrial Engineering, Cape Town, South Africa, pp. 174-181, DOI: 10.1109/ICMIMT.2019.8712027
dc.relation.haspart[Errata file]: Errata of P1
dc.relation.ispartofseriesAalto University publication series DOCTORAL DISSERTATIONSen
dc.relation.ispartofseries104/2019
dc.revCouto Barone, Dante Augusto, Prof., Federal University of Rio Grande do Sul Porto Alegre, Brazil
dc.revLamouri, Samir, Prof., ENSAM, Arts et métiers ParisTech., France
dc.subject.keyworddata-driven decision-makingen
dc.subject.keywordIoTen
dc.subject.keywordmaintenanceen
dc.subject.keyworddata qualityen
dc.subject.keywordinteroperabilityen
dc.subject.keyworddata extractionen
dc.subject.otherComputer scienceen
dc.titleMoving towards data-driven decision-making in maintenanceen
dc.typeG5 Artikkeliväitöskirjafi
dc.type.dcmitypetexten
dc.type.ontasotDoctoral dissertation (article-based)en
dc.type.ontasotVäitöskirja (artikkeli)fi
local.aalto.acrisexportstatuschecked 2019-07-02_1511
local.aalto.archiveyes
local.aalto.formfolder2019_05_23_klo_10_32
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