Fehlerprognose mit hybridem Anomalie-Management

Erhöhung der Verfügbarkeit in automatisierten Systemen

  • Manuel Bordasch Universität Stuttgart IAS

Abstract

Dieser Artikel stellt ein Konzept zur Fehlerprävention in automatisierten Systemen vor. Ziel ist es, die mittlere Ausfallzeit zu erhöhen und gleichzeitig die mittlere Reparaturzeit durch prädiktive Identifikation und Inspektion von Fehlerentwicklungen zur Laufzeit zu verringern. Darüber hinaus zielt dieses Konzept darauf ab, die Komplexität von Präventionsprozessen und die zunehmende Datenmenge im Vergleich zu rein anomaliebasierten Fehlerpräventionskonzepten zu reduzieren. Dafür wurde ein hybrides Konzept entwickelt. Dazu gehört zum einen eine fehlerbasierte Methodik, die es ermöglicht, Fehlerentwicklungsmerkmale nach dem Auftreten eines einzelnen Fehlers zu identifizieren. Mittels dieser Information kann dasselbe System oder ähnliche Systeme in Zukunft auf diese spezielle Fehlerentwicklung überprüft werden, um das Wiederauftreten dieses bestimmten Fehlers zu vermeiden. Zum anderen wird ein anomaliebasierter Überprüfungsprozess vorgestellt, um optional auch für unbekannte Fehlerentwicklungen eine Prognose
gewährleisten zu können. So trägt das hier vorgestellte hybride Anomalie-Management insgesamt dazu bei, die Verfügbarkeit der Systeme zu erhöhen.

References

  1. Mubarak, H., & Göhner, P. (2010, July). An agent-oriented approach for self-management of industrial automation systems. In Industrial Informatics (INDIN), 2010 8th IEEE International Conference on (pp. 721-726). IEEE.
  2. Böttcher, B., Badinger, J., Moriz, N., & Niggemann, O. (2013, September). Design of industrial automation systems—Formal requirements in the engineering process. In Emerging Technologies & Factory Automation (ETFA), 2013 IEEE 18th Conference on (pp. 1-4). IEEE.
  3. Laprie, J. C. (1995). Dependability—its attributes, impairments and means. In Predictably Dependable Computing Systems (pp. 3-18). Springer, Berlin, Heidelberg.
  4. Meeker, W. Q., & Hong, Y. (2014). Reliability meets big data: opportunities and challenges. Quality Engineering, 26(1), 102-116.
  5. International Atomic Energy Agency IAEA. (2000, März). Effective handling of software anomalies in computer based systems at nuclear power plants, pp. 3. Abgerufen von: https://www-pub.iaea.org/MTCD/Publications/PDF/te_1140_prn.pdf
  6. ISO 26262. (2012). Road vehicles – Functional safety. ISO: www.iso.org
  7. Avizienis, A., Laprie, J. C., Randell, B., & Landwehr, C. (2004). Basic concepts and taxonomy of dependable and secure computing. IEEE transactions on dependable and secure computing, 1(1), 11-33.
  8. Palade, V., & Bocaniala, C. D. (Eds.). (2006). Computational intelligence in fault diagnosis. Springer Science & Business Media.
  9. Isermann, R. (2006). Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer Science & Business Media.
  10. Alwi, H., Edwards, C., & Tan, C. P. (2011). Sliding Mode Observers for Fault Detection. In Fault Detection and Fault-Tolerant Control Using Sliding Modes (pp. 53-98). Springer, London.
  11. Sim, S. H., & Endrenyi, J. (1988). Optimal preventive maintenance with repair. IEEE Transactions on Reliability, 37(1), 92-96.
  12. Mladenovic, I., & Weindl, C. (2009, June). Empiric approach to a condition-oriented maintenance and investment strategy for MV cable networks. In Clean Electrical Power, 2009 International Conference on (pp. 705-709). IEEE.
  13. Tan, S. C., & Lim, C. P. (2000). Condition monitoring and fault prediction via an adaptive neural network. In TENCON 2000. Proceedings (Vol. 1, pp. 13-17). IEEE.
  14. Nan, C., Khan, F., & Iqbal, M. T. (2007, April). Abnormal process condition prediction (fault diagnosis) using G2 expert system. In Electrical and Computer Engineering, 2007. CCECE 2007. Canadian Conference on (pp. 1507-1510). IEEE.
  15. Folmer, J., Weißenberger, B., Vogel-Heuser, B., & Meyer, H. (2012, September). Diagnosis of automation devices based on engineering and historical data. In Emerging Technologies & Factory Automation (ETFA), 2012 IEEE 17th Conference on (pp. 1-4). IEEE.
  16. Yan, C., Zhang, H., & Wu, L. (2010, June). Fault prognosis of steam turbine generator set by trend analysis of frequency. In Future Power and Energy Engineering (ICFPEE), 2010 International Conference on (pp. 63-66). IEEE.
  17. Noreesuwan, T., & Suksawat, B. (2010, October). Propose of unsealed deep groove ball bearing condition monitoring using sound analysis and fuzzy logic. In Control Automation and Systems (ICCAS), 2010 International Conference on (pp. 409-413). IEEE.
  18. Marjanović, A., Kvaščev, G., Tadić, P., & Đurović, Ž. (2011). Applications of predictive maintenance techniques in industrial systems. Serbian Journal of Electrical Engineering, 8(3), 263-279.
  19. Benbouzid, M. E. H., & Nejjari, H. (2001). A simple fuzzy logic approach for induction motors stator condition monitoring. In Electric Machines and Drives Conference, 2001. IEMDC 2001. IEEE International (pp. 634-639). IEEE.
  20. Penya, Y. K., Bringas, P. G., & Zabala, A. (2008, July). Advanced fault prediction in high-precision foundry production. In Industrial Informatics, 2008. INDIN 2008. 6th IEEE International Conference on (pp. 1672-1677). IEEE.
  21. Zhang, X. H., & Kang, J. S. (2010, September). Hidden Markov models in bearing fault diagnosis and prognosis. In Computational Intelligence and Natural Computing Proceedings (CINC), 2010 Second International Conference on (Vol. 2, p. 364).
  22. Toubakh, H., & Sayed-Mouchaweh, M. (2014). Advanced data mining approach for wind turbines fault prediction. In Proceedings of second European conference of the prognostics and health management society (Vol. 5, pp. 288-296).
  23. Jiye, S., & Wei, G. (2010, December). A state searching method for model-based diagnosis. In Apperceiving Computing and Intelligence Analysis (ICACIA), 2010 International Conference on (pp. 466-469). IEEE.
  24. Baqqar, M., Ahmed, M., & Gu, F. (2011, September). Data mining for gearbox condition monitoring. In Automation and Computing (ICAC), 2011 17th International Conference on (pp. 138-142). IEEE.
  25. Niggemann, O. (2014). On the Importance of Model Learning for Intelligent Automation Systems. Expertenforum „Agenten im Umfeld von Industrie, 4, 2-4.
  26. Bordasch, M., & Gohner, P. (2013, November). Fault prevention in industrial automation systems by means of a functional model and a hybrid abnormity identification concept. In Industrial Electronics Society, IECON 2013-39th Annual Conference of the IEEE (pp. 2845-2850). IEEE.
  27. Rocchi, M., Mosciaro, F., Grottesi, F., Scortichini, M., Giantomassi, A., Pirro, M., ... & Ippoliti, G. (2014, September). Fault prognosis for rotating electrical machines monitoring using recursive least square. In Education and Research Conference (EDERC), 2014 6th European Embedded Design in (pp. 269-273). IEEE.
  28. Givehchi, O., & Jasperneite, J. (2013). Industrial automation services as part of the Cloud: First experiences. Proceedings of the Jahreskolloquium Kommunikation in der Automation–KommA, Magdeburg.
  29. Mattern, F., & Floerkemeier, C. (2010). From the Internet of Computers to the Internet of Things. In From active data management to event-based systems and more (pp. 242-259). Springer, Berlin, Heidelberg.
  30. Göhner, P. (2012). Automatisierungstechnik 1, Vorlesungsskript, Institut für Automatisierungs- und Softwaretechnik, 2012, S. 15-40.
  31. Bordasch, M. (2016). Abnormitäten-Management zur Fehlerprävention bei automatisierten Systemen im Betrieb, (pp. 63-76). Shaker Verlag.
  32. Bordasch, M., Jazdi, N., Göhner, P. (2014). Abnormality management for fault prevention in industrial automation systems. In ICCMA 2014, 2nd International Conference on Control, Mechatronics and Automation, S. 2, Dubai. ICCMA.
  33. Tooley, M. (2009). Wyatt, Aircraft Electrical and Electronic Systems: Principles, Maintenance and Operation.
  34. Yu, M., Okhtar, H., Merabti, M. (2007). Fault management in wireless sensor networks. In IEEE Wireless Communications, 14(6), 13-19.
  35. Lauber, R., Göhner, P. (1999). Prozessautomatisierung 2 (pp. 226-228). Berlin, Heidelberg, Springer-Verlag.
  36. Bari, N., Mani, G., & Berkovich, S. (2013, July). Internet of things as a methodological concept. In Computing for Geospatial Research and Application (COM. Geo), 2013 Fourth International Conference on (pp. 48-55). IEEE.
  37. Patton, R. J. (1991). Fault detection and diagnosis in aerospace systems using analytical redundancy. Computing & Control Engineering Journal, 2(3), 127-136.
  38. Hayes, M. A., & Capretz, M. A. (2015). Contextual anomaly detection framework for big sensor data. Journal of Big Data, 2(1), 2.
  39. Dikaiakos, M. D., Katsaros, D., Mehra, P., Pallis, G., & Vakali, A. (2009). Cloud computing: Distributed internet computing for IT and scientific research. IEEE Internet computing, 13(5).
  40. de Oliveira Cavalcanti, A. L., de Souza, A. J., Silva, D., Rocha, G., & Francisco Filho, S. D. L. (2009, June). Integrating mobile devices and industrial automation through web services. In Industrial Informatics, 2009. INDIN 2009. 7th IEEE International Conference on (pp. 173-176). IEEE.
  41. Festo Didactic, Adiro Automatisierungstechnik GmbH. (2008). MPS-PA Compact Workstation (pp. 34). Abgerufen von: https://de.scribd.com/document/345778410/Manual-MPS-PA-Compact-Workstation-EN-pdf
Veröffentlicht
2019-03-19
Zitieren
BORDASCH, Manuel. Fehlerprognose mit hybridem Anomalie-Management. atp magazin, [S.l.], v. 61, n. 3, p. 64-75, märz 2019. ISSN 2364-3137. Verfügbar unter: <http://ojs.di-verlag.de/index.php/atp_edition/article/view/2357>. Date accessed: 23 apr. 2019. doi: https://doi.org/10.17560/atp.v61i3.2357.
Rubrik
Hauptbeitrag / Peer-Review