ABSTRACT
We present the first system, called ICM-Wind, for semantics-empowered fluid condition monitoring (FCM) in wind turbines. It monitors the condition of fluids in the wind turbine gearbox, recognizes actual and the onset of failures of FCM sensors and components installed on the gearbox, and provides knowledge-based failure diagnosis support to non-experts. For this purpose, the ICM-Wind system performs semantic sensor data analysis by applying semantic technologies for interpreting the state of turbine parts and answering questions related to their maintenance. Domain knowledge is encoded in OWL2 and with SPIN rules. Fault detection and diagnosis queries are answered by use of the semantic reasoners Fact++, STAR, TopSPIN rule engine, and SwiftOWLIM store. The system prototype was successfully tested in cooperation with the HYDAC Filter Systems GmbH based on given selected samples of a two-year recording of FCM multi-sensor and operational data for two wind turbines of a regional on-shore wind farm operated by the ABO Wind AG.
- Andersson, C.; Witfelt, C. (2000): Advisor: A Prolog implementation of an automated nerual network for diagnosis of rotating machinery. http://www.fcss.ukma.kiev.Ua/courses/IN.B.09/VIP52PE/HTML/vip/articles/carstenanderson/Google Scholar
- Calbimonte, JP.; Jeung, H.; Corcho, O.; Aberer, K. (2011): Semantic sensor data search in a large-scale federated sensor network. Proc. 4th Intl. Workshop on Semantic Sensor Networks.Google Scholar
- Compton, M.; et al. (2012): The SSN ontology of the W3C semantic sensor network incubator group. Web Semantics, 17. Google ScholarDigital Library
- Guenel, A.; Meshram, A.; Bley, T.; Schuetze, A.; Klusch, M. (2013): Statistical and Semantic Multisensor Data Evaluation for Fluid Condition Monitoring in Wind Turbines. Proc. 16th Intl. Conf. on Sensors and Measurement Technology, Germany.Google Scholar
- Hameed, Z.; Hong, Y. S.; Cho, Y. M.; Ahn, s. H.; Song, O. K. (2009): Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable and Sustainable Energy Reviews, 13 (1).Google Scholar
- ISO TC 108/SC 5 (2005): Condition monitoring and diagnostics of machines - Data processing, communication, and presentation. Part 2: Data processing. Standard ISO 13374--2: 2005.Google Scholar
- Jin, G.; Xiang, Z; Lv, F. (2009): Semantic integrated condition monitoring and maintenance of complex system. Proc. 16th Intl. Conf. on Industrial Engineering and Engineering Management.Google ScholarCross Ref
- Kasneci, G., et al. (2009): STAR: Steiner Tree Approximation in Relationship-Graphs. Proc. 25th IEEE Intl. Conf. on Data Engineering (ICDE). Google ScholarDigital Library
- Lu, B.; Li, Y.; Wu, X.; Yang, Z. (2009): A review of recent advances in wind turbine condition monitoring and fault diagnosis. Proc. IEEE Conf. on Power Electronics and Machines in Wind Applications.Google ScholarCross Ref
- Mechefske, C. K. (2005): Machine condition monitoring and fault diagnostics. In: Vibration and Shock Handbook. De Silva, W. (ed.), Ch. 25, CRC Press.Google Scholar
- Rafieea, J.; Arvania, F.; Harifib, A.; Sadeghic, M. H. (2007): Intelligent condition monitoring of a gearbox using artificial neural network. Mechanical Systems and Signal Processing, 21(4), Elsevier.Google Scholar
- Rudd, S. E.; Catterson, V. M.; McArthur, S. D. J. (2007): Agent-based technology for data management, diagnostics and learning within condition monitoring applications. Proc. 4th Intl. Conf. on Condition Monitoring.Google Scholar
- ter Horst, H. J. (2005): Combining RDF and Part of OWL with Rules: Semantics, Decidability, Complexity. Proc. Int. Semantic Web Conf., LNCS 3729, Springer. Google ScholarDigital Library
- Trave-Massuyes, L.; Milne, R. (1997): Gas-turbine condition monitoring using qualitative model-based diagnosis. IEEE Expert, 12(3). Google ScholarDigital Library
- Walford, C.; Roberts, D. (2006): Condition monitoring of wind turbines - technology overview, seeded-fault testing, and cost-benefit analysis. Electrical Power Research Institute (EPRI), Tech. Rep. 1010419, Palo Alto (CA), USA.Google Scholar
- Yang, W.; Tavner, P. J.; Crabtree, C. J.; Wilkinson, M. (2010): Cost-effective condition monitoring for wind turbines. IEEE Trans. Industrial Electronics, 57(1).Google Scholar
- Zhijing, Y. et al. (2011): Intelligent condition monitoring via sparse representation and principal component analysis for industrial gas turbines. Proc. Intl. Conf. on Mech. Eng. and Technology, London, UK.Google Scholar
- The Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE): http://www.opengeospatial.org/projects/groups/sensorwebdwgGoogle Scholar
- On-shore wind farm in Marpingen (Saarland, Germany) operated by the ABO Wind AG: http://www.abowind.com/com/operational-management/germany-marpingen-wind-farm.htmlGoogle Scholar
- Fluid condition monitoring by HYDAC: CM-Expert http://www.hydac.com/de-en/service/fluid-engineering/condition-monitoring/product-program/monitoringcontrol.htmlGoogle Scholar
Index Terms
- ICM-Wind: semantics-empowered fluid condition monitoring of wind turbines
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