Avionics Maintenance Ontology Building for Failure Diagnosis Support

Luis Palacios, Gaëlle Lortal, Claire Laudy, Christian Sannino, Ludovic Simon, Giuseppe Fusco, Yue Ma, Chantal Reynaud

Abstract

In the aviation industry, the delay in maintaining or recovering aircrafts heavily impacts the profit of an airline company. Consequently the maintenance actions identification and planning of aircrafts is crucial. However, due to the complexity of the domain in terms of data sources, distributed systems and information availability, it is hard to provide automatic maintenance support. We propose to use semantic technologies to model the domain at a conceptual level through ontology, thus abstracting from the data sources and the maintenancers’uses and jobs. In this manner the information relevant for characterizing failures and maintenance events is encapsulated and provided to end users via an easier access, which otherwise would be inaccessible or would require expert analysis to obtain. Such a formal model of the domain can furthermore enable automated reasoning for maintenance discovery and failure causes detection by integrating a large amount of background contextual information scattering in different resources. In this paper we provide the rationale of the Avionics Maintenance ontology i.e. how we built it through expert knowledge and alignment of different sources and an ontology alignment evaluation tool.

References

  1. Castano, S., Ferrara, A., and Montanelli, S. (2003). Hmatch: an algorithm for dynamically matching ontologies in peer-based systems. In Proc.1st Int.Conf. on Semantic Web and DB (pp. 218-237).
  2. Chein, M. and Mugnier, M.-L. (2008). Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs. Springer.
  3. Cimiano, P., Hotho, A., and Staab, S., 2005, Learning concept hierarchies from text corpora using formal concept analysis. J. Artif. Int. Res., 24(1):305-339.
  4. Danping Z, Youyuan W, Qi Z, Hongsheng J, Xianming D (2012). The Design of the Aviation Products Semantic Information System Based on Ontology. 2012 Int. Workshop on Information and Electronics Engineering
  5. Euzenat, J., and Shvaiko, P. (2007). Ontology matching (Vol. 18). Heidelberg: Springer.
  6. Faria, D., Pesquita, C., Santos, E., Cruz, I. F., and Couto, F. M. (2013). Agreement maker light results for OAEI 2013. In Proceedings of the 8th Int. Conf.e on Ontology Matching-Volume 1111 (pp. 101-108).
  7. Giunchiglia, F., Autayeu, A., and Pane, J. (2012). SMatch: an open source framework for matching lightweight ontologies. Semantic Web, 3(3), 307-317.
  8. ISO/TS 15926-8:2011 http://www.iso.org/ catalogue_detail.htm?csnumber=52456
  9. Jiménez-Ruiz, E., Grau, B. C., Zhou, Y., and Horrocks, I. (2012). Large-scale Interactive Ontology Matching: Algorithms and Implementation. In ECAI (Vol. 242, pp. 444-449)
  10. Laudy, C. (2015). Hidden relationships discovery through high-level information fusion. FUSION 2015: 916-923
  11. Marshall, J. R., and Morris, A. T. (2007). Organization's Orderly Interest Exploration: Inception, Development and Insights of AIAA's Topics Database. 20pages.
  12. McGuinness, D. L., and Van Harmelen, F. (2004). OWL web ontology language overview. W3C recommendation, 10(10), 2004.
  13. Neff, J. M., Some, R., and Lyke, J. (2007). Lessons Learned in Building a Spacecraft XML Taxonomy and Ontology. 16 pages.
  14. NF EN 13306, http://maint.t.i.b.free.fr/Files/Other/ NF%20EN%2013306.pdf
  15. Otero-Cerdeira, L., Rodríguez-Martínez, F. J., and Gómez-Rodríguez, A. (2015). Ontology matching: A literature review. Expert Sys. App., 42(2), 949-971.
  16. Ponzetto, S. and Strube, M.,. Taxonomy induction based on a collaboratively built knowledge repository. volume 9 of 175, pages 1737-1756. 2011.
  17. Putten van BJ, Wolf SR, Dignum V (2008). An Ontology for Traffic Flow Management. 26th Congress of International Council of the Aeronautical Sciences
  18. Safar, B., and Reynaud, C. (2009). Alignement d'ontologies basé sur des ressources complémentaires : TaxoMap. TSI, 28(10), 1211-1232.
  19. Sowa, JF (1984) Conceptual Structures - Information Processing in Mind and Machine. The Systems Programming Series, Addison-Wesley
Download


Paper Citation


in Harvard Style

Palacios L., Lortal G., Laudy C., Sannino C., Simon L., Fusco G., Ma Y. and Reynaud C. (2016). Avionics Maintenance Ontology Building for Failure Diagnosis Support . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2016) ISBN 978-989-758-203-5, pages 204-209. DOI: 10.5220/0006092002040209


in Bibtex Style

@conference{keod16,
author={Luis Palacios and Gaëlle Lortal and Claire Laudy and Christian Sannino and Ludovic Simon and Giuseppe Fusco and Yue Ma and Chantal Reynaud},
title={Avionics Maintenance Ontology Building for Failure Diagnosis Support},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2016)},
year={2016},
pages={204-209},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006092002040209},
isbn={978-989-758-203-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2016)
TI - Avionics Maintenance Ontology Building for Failure Diagnosis Support
SN - 978-989-758-203-5
AU - Palacios L.
AU - Lortal G.
AU - Laudy C.
AU - Sannino C.
AU - Simon L.
AU - Fusco G.
AU - Ma Y.
AU - Reynaud C.
PY - 2016
SP - 204
EP - 209
DO - 10.5220/0006092002040209