Open Access
Issue
MATEC Web Conf.
Volume 349, 2021
6th International Conference of Engineering Against Failure (ICEAF-VI 2021)
Article Number 03006
Number of page(s) 8
Section Components and Structural Elements in Engineering Applications: Design, Detections of Defects, Structural Health Monitoring
DOI https://doi.org/10.1051/matecconf/202134903006
Published online 15 November 2021
  1. Liao, S. H. Knowledge management technologies and applications — literature review from 1995 to 2002. Expert systems with applications, 25(2),155-164 (2003). [CrossRef] [Google Scholar]
  2. Radanliev, P., De Roure, D., Walton, R., Van Kleek, M., Montalvo, R. M., Santos, O., … & Anthi, E. Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge. SN Applied Sciences, 2(11), pp. 1-8 (2020). [CrossRef] [Google Scholar]
  3. Nagaraja Rao, B. K., The Role of Artificial Intelligence (AI) in Condition Monitoring and Diagnostic Engineering Management (COMADEM): A Literature Survey. American Journal of Artificial Intelligence. Vol. 5, No. 1, pp. 17-37 (2021) [CrossRef] [Google Scholar]
  4. Talamo, C., Paganin, G., & Rota, F. Industry 4.0 for failure information management within Proactive Maintenance. In IOP Conference Series: Earth and Environmental Science (Vol. 296, No. 1, p. 012055). IOP Publishing, (2019, July). [CrossRef] [Google Scholar]
  5. Ansari, F. Cost-based text understanding to improve maintenance knowledge intelligence in manufacturing enterprises. Computers & Industrial Engineering, 141, 106319 (2020) [CrossRef] [Google Scholar]
  6. Castellanos, V., Albiter, A., Hernández, P., & Barrera, G. Failure analysis expert system for onshore pipelines. Part–I: Structured database and knowledge acquisition. Expert Systems with Applications, 38(9),11085-11090, (2011). [CrossRef] [Google Scholar]
  7. Mourtzis, D., Vlachou, E., Milas, N., & Xanthopoulos, N. A cloud-based approach for maintenance of machine tools and equipment based on shop-floor monitoring. Procedia Cirp, 41, 655-660, (2016). [Google Scholar]
  8. James, A. T., Gandhi, O. P., & Deshmukh, S. G. Knowledge management of automobile system failures through development of failure knowledge ontology from maintenance experience. Journal of Advances in Management Research. (2017) [Google Scholar]
  9. P.J. Graham-Jones, B.G Mellor, Eng. Failure Analysis 2(2), 137-149 (1995) [CrossRef] [Google Scholar]
  10. Wichawong, P., & Chongstitvatana, P. Knowledge management system for failure analysis in hard disk using case-based reasoning. 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD, IEEE, pp. 1-6 (2017) [Google Scholar]
  11. Lucene.NET, https://lucenenet.apache.org [Google Scholar]
  12. Tang A, Tam R, Cadrin-Chenevert A, Guest W, Chong J, Barfett J, Cheplev L, Cairns R, Mitchell R, Cicero M, Poudrette MG, Jaremko JL, Reinhold C, Gallix B, Gray B, Geis R., Can Assoc Radiol J., 69 120–35 (2018) [CrossRef] [Google Scholar]
  13. Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Radiographics 37 (2),505–15 (2017) [CrossRef] [Google Scholar]
  14. C. Rainey, J. McConnell, C. Hughes, R. Bond, S. McFadden Intelligence-Based Medicine 5 100033 (2021) [Google Scholar]
  15. Zablith, Fouad; Antoniou, Grigoris; d’Aquin, Mathieu; Flouris, Giorgos; Kondylakis, Haridimos and Motta,Enrico. Ontology evolution: a process-centric survey. The Knowledge Engineering Review, 30(1) pp. 45–75 (2015). [CrossRef] [Google Scholar]
  16. Zablith, Fouad (2009). Evolva: a comprehensive approach to ontology evolution. In: 6th European Semantic Web Conference, 31 May - 4 Jun 2009, Heraklion, Crete, Greece, Springer-Verlag, pp.944–948, (ESWC 2009) [Google Scholar]
  17. Presutti, V., Daga, E., Gangemi, A., & Blomqvist, E. eXtreme design with content ontology design patterns. In Proc. Workshop on Ontology Patterns. (2009, October) [Google Scholar]
  18. Giannakopoulos, G., Mavridi, P., Paliouras, G., Papadakis, G., & Tserpes, K. Representation models for text classification: a comparative analysis over three web document types. In Proceedings of the 2nd international conference on web intelligence, mining and semantics (p. 13). ACM (2012). [Google Scholar]
  19. Karalis, D.G., Melanitis, N.Ε., Yannoulis, Y.G., Failure analysis of a Cu–12Mn mechanical fastener in marine environment, Eng. Failure Analysis, 94, pp. 69–77 (2018) [CrossRef] [Google Scholar]
  20. Lekatou, A.G., Mpalanou, M., Lentzaris, K., Karantzalis, A.E., Melanitis, N., Microstructure and surface degradation of Al reinforced by AlxW intermetallic compounds via different fabrication routes, MATEC Web of Conferences, 188, 03001 (2018) [CrossRef] [EDP Sciences] [Google Scholar]
  21. Karalis, D.G., Melanitis, N.E., Analysis of a premature failure of a hub from a diesel generator of a high-speed motor ship, Journal of Failure Analysis and Prevention, 14 (2),pp. 236–246 (2014) [CrossRef] [Google Scholar]
  22. Paipetis, A.S., Aggelis, D.G., Barkoula, N.M., Matikas, T.E., Melanitis, N., Damage monitoring of composite laminates using ultrasonics, Emerging Technologies in Non-Destructive Testing V - Proceedings of the 5th Conference on Emerging Technologies in NDT, pp. 281–286 (2012) [Google Scholar]
  23. Karalis, D.G., Melanitis, N.E., Pantelis, D.I., Failure analysis of a rock anchor made of stainless steel in marine environment, Eng. Failure Analysis, 19(1), pp. 123–130 (2012) [CrossRef] [Google Scholar]
  24. https://protege.stanford.edu/, https://protege.stanford.edu/about.php#citing [Google Scholar]
  25. Musen, M.A. The Protégé project: A look back and a look forward. AI Matters. Assoc. of Computing Machinery Specific Interest Group in Artificial Intelligence, 1(4), (2015). [Google Scholar]

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