Abstract
Industry today employs rule-based diagnostic systems to minimize the maintenance cost and downtime of equipment. Rules are typically used to process signals from sensors installed in equipment by filtering, aggregating, and combining sequences of time-stamped measurements recorded by the sensors. Such rules are often data-dependent in the sense that they rely on specific characteristics of individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers especially when the rules require domain knowledge. In this work we propose an approach to address these problems by relying on the well-known Ontology-Based Data Access approach: we propose to use ontologies to mediate the sensor signals and the rules. To this end, we propose a semantic rule language, SDRL, where signals are first class citizens. Our language offers a balance of expressive power, usability, and efficiency: it captures most of Siemens data-driven diagnostic rules, significantly simplifies authoring of diagnostic tasks, and allows to efficiently rewrite semantic rules from ontologies to data and execute over data. We implemented our approach in a semantic diagnostic system and evaluated it. For evaluation we developed a use case of rail systems at Siemens and conducted experiments to demonstrate both usability and efficiency of our solution.
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References
Artale, A., Kontchakov, R., Ryzhikov, V., Zakharyaschev, M.: The complexity of clausal fragments of LTL. In: McMillan, K., Middeldorp, A., Voronkov, A. (eds.) LPAR 2013. LNCS, vol. 8312, pp. 35–52. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45221-5_3
Artale, A., Kontchakov, R., Wolter, F., Zakharyaschev, M.: Temporal description logic for ontology-based data access. In: IJCAI 2013 (2013)
Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds).: The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, Cambridge (2003)
Brandt, S., Kalaycı, E., Kontchakov, R., Ryzhikov, V., Xiao, G., Zakharyaschev, M.: Ontology-based data access with a horn fragment of metric temporal logic. In: AAAI (2017)
Calvanese, D., et al.: Ontop: answering SPARQL queries over relational databases. JWS 8(3), 471–487 (2017)
Calvanese, D., et al.: The MASTRO system for ontology-based data access. JWS 2(1), 43–53 (2011)
Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Tractable reasoning and efficient query answering in description logics: the DL-lite family. JAR 39(3), 385–429 (2007)
Charron, B., Hirate, Y., Purcell, D., Rezk, M.: Extracting semantic information for e-Commerce. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9982, pp. 273–290. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46547-0_27
Corcho, O., Calbimonte, J., Jeung, H., Aberer, K.: Enabling query technologies for the semantic sensor web. Int. J. Semant. Web Inf. Syst. 8(1), 43–63 (2012)
Horrocks, I., Giese, M., Kharlamov, E., Waaler, A.: Using semantic technology to tame the data variety challenge. IEEE Int. Comput. 20(6), 62–66 (2016)
Kharlamov, E.: Ontology based access to exploration data at statoil. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 93–112. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25010-6_6
Kharlamov, E.: Optique: towards OBDA systems for industry. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds.) ESWC 2013. LNCS, vol. 7955, pp. 125–140. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41242-4_11
Kharlamov, E.: How semantic technologies can enhance data access at siemens energy. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 601–619. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11964-9_38
Koymans, R.: Specifying real-time properties with metric temporal logic. Real-Time Syst. 2(4), 255–299 (1990)
Mehdi, G., Brandt, S., Roshchin, M., Runkler, T.A.: Semantic framework for industrial analytics and diagnostics. In: IJCAI (2016)
Mehdi, G., Brandt, S., Roshchin, M., Runkler, T.A.: Towards semantic reasoning in knowledge management systems. In: AI for Knowledge Management Workshop at IJCAI (2016)
Poggi, A., Lembo, D., Calvanese, D., De Giacomo, G., Lenzerini, M., Rosati, R.: Linking data to ontologies. J. Data Semant. 10, 133–173 (2008)
Rao, B.: Handbook of Condition Monitoring. Elsevier, Oxford (1996)
Vachtsevanos, G., Lewis, F.L., Roemer, M., Hess, A., Wu, B.: Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Wiley, Hoboken (2006)
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This research is partially supported by the Free University of Bozen-Bolzano projects QUEST, ROBAST and QUADRO.
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Kharlamov, E. et al. (2018). Diagnostics of Trains with Semantic Diagnostics Rules. In: Riguzzi, F., Bellodi, E., Zese, R. (eds) Inductive Logic Programming. ILP 2018. Lecture Notes in Computer Science(), vol 11105. Springer, Cham. https://doi.org/10.1007/978-3-319-99960-9_4
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