Abstract
In data management, especially when working with significant amount of data as characteristic for NDE, key for an efficient and reliable workflow is the interoperability between data. For advantageous use of using computerized maintenance management systems (CMMS) for future NDE 4.0 data management frameworks, semantic interoperability needs to be reached to ensure a smooth data flow throughout the whole process. However, it is of crucial importance to properly select, implement, and utilize CMMS beforehand. To date, no system has been implemented which pursues a completely holistic approach in all aspects of a CMMS solution. Database models of existing CMMS are based on classic relational database architectures which are missing interconnectivity. A holistic data management is especially required for NDE 4.0 in all industries with high reliability demands, for example, energy conversion and distribution, infrastructures, railway, aviation, public transportation, and the manufacturing and process industry.
Existing CMMS solutions focus on explicit strengths to analyze single components of a physical asset in a technological way. Superior data analytics focusing on chain of effects and tracking of root causes integrated throughout the whole plant life cycle do not exist so far. However, this would be a major step toward gaining a complete understanding to optimize asset maintenance management strategies and tactics. The following chapter introduces the key facts using examples from the wind industry.
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References
Moubray J. Reliability-centred maintenance. Repr. Oxford: Butterworth-Heinemann; 1995. ISBN 978-0750602303.
The Institute of Asset Management, editor. IAM – knowledge. 2017. Available online at https://theiam.org/knowledge/. Checked on 5/23/2018.
Kharlamov E, Solomakhina N, Özcep Ö, Zheleznyakov D, Hubauer T, Lamparter, S et al. How Semantic Technologies can Enhance Data Access at Siemens Energy. In: Proceedings of the International Semantic Web Conference 2014. 2014. p. 601–19.
Horrocks I. What are ontologies good for? In: Evolution of semantic systems. 2013. p. 175–88.
Poggi A, Lembo D, Calvanese D, De Giacomo G, Lenzerini M, Rosati R. Linking data to ontologies. J Data Semantics (X). 2008;133–73. https://doi.org/10.1007/978-3-540-77688-8_5.
Compton M, Barnaghi BM, Bermudez L, Garcia-Castro R, Corcho O, Cox S et al. The SSN Ontology of the W3C Semantic Sensor Network Incubator Group. In: Web Semantics: Science, Services and Agents on the World Wide Web. 2012;(17):25–32. Available online at https://ac.els-cdn.com/S1570826812000571/1-s2.0-S1570826812000571-main.pdf?_tid=416a8c3b-f720-4126-8228-2b3762428490&acdnat=1534929833_37296b04b19dc1cfb9011c29a3a79378. Checked on 8/22/2018.
Minsky ML. Semantic information processing. Cambridge/London: The MIT Press; 2015.
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Geiss, C.T., Gramlich, M. (2021). Semantic Interoperability as Key for a NDE 4.0 Data Management. In: Meyendorf, N., Ida, N., Singh, R., Vrana, J. (eds) Handbook of Nondestructive Evaluation 4.0. Springer, Cham. https://doi.org/10.1007/978-3-030-48200-8_4-1
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DOI: https://doi.org/10.1007/978-3-030-48200-8_4-1
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