Abstract
The reliability of quality management in industrial processes mainly depends on information about the traceability, precision and accuracy of a measurement system as well as on its systematic bias. The progressive development of networking and sensing in industrial machinery facilitates a quality-related process monitoring regarding information of measurement systems and singular sensor nodes. Hereby, integration on the information level is mandatory. Furthermore, information from the shop floor and from enterprise applications is needed to provide a consistent and integrated quality analysis. Thereby, these systems use different standards and technologies for exportation and propagation of data. Besides, integrative quality management and data analysis require enriched data that does not only comprise, for example, the measured value and its standard-dependent unit on the sensor level; rather, additional information is needed (e.g. the production process or the time and place of measurement). In this paper, a framework is presented that facilitates the semantic integration and analysis of measurement and enterprise data according to real-time requirements. Semantic technologies are used to encode the meaning of the data from the application code. Herewith, the data is automatically annotated using terms and concepts taken from the application domain. Furthermore, a semantic integration and transformation process is facilitated. Thus, subsequent integration and, most importantly, analysis processes can take advantage of these terms and concepts using specialized analysis algorithms. Besides, the conceptual application of the presented framework and processes in a high-pressure-die-casting scenario is presented.
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I.F. Akyildiz, T. Melodia, K.R. Chowdhury, A survey on wireless multimedia sensor networks. Computer Networks 51 (4), 2007, pp. 921–960
H. Kagermann, W. Wahlster, J. Helbig. Umsetzungsempfehlungen für das zukunftsprojekt industrie 4.0, abschlussbericht des arbeitskreises industrie 4.0, 2012. Vorabversion
N. Trigoni, B. Krishnamachari, Sensor network algorithms and applications. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370 (1958), 2012, pp. 5–10
V.C. Gungor, G.P. Hancke, Industrial wireless sensor networks: Challenges, design principles, and technical approaches. IEEE Transactions on Industrial Electronics 56 (10), 2009, pp. 4258–4265
T. Kröger, F.M. Wahl, Multi-sensor integration and sensor fusion in industrial manipulation: Hybrid switched control, trajectory generation, and software development. In: Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. 2008, pp. 411–418
K. Römer, O. Kasten, F. Mattern, Middleware challenges for wireless sensor networks. ACM SIGMOBILE Mobile Computing and Communications Review 6 (4), 2002, pp. 59–61
K. Aberer, M. Hauswirth, A. Salehi, Infrastructure for data processing in large-scale interconnected sensor networks. In: International Conference on Mobile Data Management, 2007. IEEE, Piscataway, NJ, 2007, pp. 198–205
S. Hadim, N. Mohamed, Middleware: Middleware challenges and approaches for wireless sensor networks. Distributed Systems Online, IEEE 7 (3), 2006, p. 1
L. Mottola, G.P. Picco, Programming wireless sensor networks: Fundamental concepts and state of the art. ACM Comput. Surv. 43 (3), 2011, pp. 19:1–19:51
J. Yick, B. Mukherjee, D. Ghosal, Wireless sensor network survey. Computer Networks 52 (12), 2008, pp. 2292–2330
L. Gurgen, C. Roncancio, C. Labbé, A. Bottaro, V. Olive, Sstreamware: a service oriented middleware for heterogeneous sensor data management. In: Proceedings of the 5th international conference on Pervasive services (ICPS ’08), ACM, New York, USA, 2008, pp. 121–130
F. Golatowski, J. Blumenthal, M. H, M. Haase, H. Burchardt, D. Timmermann, Service-oriented software architecture for sensor networks. In: In Proc. Int. Workshop on Mobile Computing. 2003, pp. 93–98
E. Avilés-López, J. García-Macías, Tinysoa: a service-oriented architecture for wireless sensor networks. Service Oriented Computing and Applications 3 (2), 2009, pp. 99–108
K.K. Khedo, R.K. Subramanian, A service-oriented component-based middleware architecture for wireless sensor networks. IJCSNS International Journal of Computer Science and Network Security 9 (3), 2009, pp. 174–182
H. Abangar, P. Barnaghi, K. Moessner, A. Nnaemego, K. Balaskandan, R. Tafazolli, A service oriented middleware architecture for wireless sensor networks. In: 2010 Future Network & Mobile Summit. IIMC International Information Management Corp., Dublin, Ireland, 2010
J. Ibbotson, C. Gibson, J. Wright, P. Waggett, P. Zerfos, B. Szymanski, D.J. Thornley, Sensors as a service oriented architecture: Middleware for sensor networks. In: Proceedings of the 2010 Sixth International Conference on Intelligent Environments, ed. by V. Callaghan. IEEE Computer Society and IEEE, Washington, DC, USA, 2010, pp. 209–214
C. Groba, I. Braun, T. Springer, M. Wollschlaeger, A service-oriented approach for increasing flexibility in manufacturing. In: IEEE International Workshop on Factory Communication Systems, 2008, ed. by G. Cena, F. Simonot-Lion. IEEE, Piscataway, NJ, 2008, pp. 415–422
N. Mohamed, J. Al-Jaroodi, A survey on service-oriented middleware for wireless sensor networks. Serv. Oriented Comput. Appl. 5 (2), 2001, pp. 71–85
K.A. Delin, S.P. Jackson, Sensor web: a new instrument concept. Proc. SPIE Functional Integration of Opto-Electro-Mechanical Devices and Systems 4284, 2001
V. Vescoukis, N. Doulamis, S. Karagiorgou, A service oriented architecture for decision support systems in environmental crisis management. Future Gener. Comput. Syst. 28 (3), 2012, pp. 593–604
R. Daisenroth, MES unterstützt Industrie 4.0: Wegweisendes Zukunftskonzept. VDI-Z 2013 (Nr 4), April, pp. 20–22
A. Bröring, P. Mauè, K. Janowicz, D. Nüst, C. Malewski, P. Maué, Semantically-enabled sensor plug & play for the sensor web. Sensors 2011 11 (12), 2011, pp. 7568–7605
J. Wright, C. Gibson, F. Bergamaschi, K. Marcus, R. Pressley, G. Verma, G. Whipps, A dynamic infrastructure for interconnecting disparate isr/istar assets (the ita sensor fabric). In: Proceedings of the 12th International Conference on Information Fusion, 2009. IEEE, Piscataway, NJ, 2009, pp. 1393–1400
W. Wang, P. Barnaghi, G. Cassar, F. Ganz, P. Navaratnam, Semantic sensor service networks. In: IEEE Sensors 2012. IEEE, 2012, pp. 1–4
M. Lewis, D. Cameron, S. Xie, B. Arpinar, Es3n: A semantic approach to data management in sensor networks. In: Semantic Sensor Networks Workshop. 2006
K. Martinez, J.K. Hart, R. Ong, K. Martinez, J.K. Hart, R. Ong, Environmental sensor networks. Computer 37 (8), 2004, pp. 50–56
U. Enste, W. Mahnke, OPC Unified Architecture: Die nächste Stufe der Interoperabilität. at - Automatisierungstechnik 59 (7), 2011, pp. 397–404
M. Compton, C.A. Henson, H. Neuhaus, L. Lefort, A.P. Sheth, A survey of the semantic specification of sensors. In: Proceedings of the 2nd International Workshop on Semantic Sensor Networks, ed. by K. Taylor, D.D. Roure. CEUR-WS.org, 2009, CEUR Workshop Proceedings, pp. 17–32
M. Compton, P.M. Barnaghi, L. Bermudez, R. Garcia-Castro, Ó. Corcho, S. Cox, J. Graybeal, M. Hauswirth, C.A. Henson, A. Herzog, V.A. Huang, K. Janowicz, W.D. Kelsey, D. Le Phuoc, L. Lefort, M. Leggieri, H. Neuhaus, A. Nikolov, K.R. Page, A. Passant, A.P. Sheth, K. Taylor, The ssn ontology of the w3c semantic sensor network incubator group. Journal of Web Semantics 17, 2012, pp. 25–32
T. Berners-Lee, Linked data – the story so far. International Journal on Semantic Web and Information Systems 5 (3), 2011, pp. 1–22
T. Meisen, Framework zur Kopplung numerischer Simulationen für die Fertigung von Stahlerzeugnissen, vol. Fortschritt-Berichte VDI. Reihe 10: Informatik / Kommunikation; 823, 1st edn. VDI-Verlag, Düsseldorf, 2012
T. Meisen, P. Meisen, D. Schilberg, S. Jeschke, Adaptive information integration: Bridging the semantic gap between numerical simulations. In: Enterprise Information Systems, Lecture Notes in Business Information Processing, vol. 102, ed. by R. Zhang, J. Zhang, Z. Zhang, J. Filipe, J. Cordeiro, Springer, Berlin / Heidelberg, 2012, pp. 51–65
D. Guinard, V. Trifa, F. Mattern, E. Wilde, From the internet of things to the web of things: Resource-oriented architecture and best practices. In: Architecting the Internet of Things, ed. by D. Uckelmann, M. Harrison, F. Michahelles, Springer Berlin / Heidelberg, Berlin, Heidelberg, 2011, pp. 97–129
Acknowledgments
The approaches presented in this paper are supported by the German Research Association (DFG) within the Cluster of Excellence “Integrative Production Technology for High-Wage Countries”. The authors would also like to thank the Audi AG for their support and contributions to the presented research.
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Meisen, T., Rix, M., Hoffmann, M., Schilberg, D., Jeschke, S. (2016). A Framework for Semantic Integration and Analysis of Measurement Data in Modern Industrial Machinery. In: Jeschke, S., Isenhardt, I., Hees, F., Henning, K. (eds) Automation, Communication and Cybernetics in Science and Engineering 2015/2016. Springer, Cham. https://doi.org/10.1007/978-3-319-42620-4_67
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