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Process-Oriented Approach for Analysis of Sensor Data from Longwall Monitoring System

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Intelligent Systems in Production Engineering and Maintenance (ISPEM 2018)

Abstract

In the paper we address possibility of industrial process analysis based on sensor data with the use of process-oriented analytic techniques. We propose in this area usage of process mining techniques. Important issue in the context of usefulness of industrial sensor data gathered in the monitoring systems for process analysis is proper level of abstraction of an event log.

We present our approach requiring creation of high-level event logs based on low-level events from longwall monitoring system in order to model and analyse the mining process in an underground mine. We use combination of unsupervised data mining techniques as well as domain knowledge to discover stages in an example process and create an event logs for further process analysis.

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References

  1. Bednarz, T., James, C.A.R., Widzyk-Capehart, E., Caris, C., Alem, L.: Distributed collaborative immersive virtual reality framework for the mining industry. In: Billingsley, J., Brett, P. (eds.) Machine Vision and Mechatronics in Practice, pp. 39–48. Springer, Berlin Heidelberg (2015)

    Google Scholar 

  2. Bose, R.P.J.C., van der Aalst, W.M.P.: Abstractions in process mining: a taxonomy of patterns. In: 7th International BPM Conference Proceedings, Germany, pp. 159–175 (2009)

    Google Scholar 

  3. Brodny, J., Alszer, S., Krystek, J., Tutak, M.: Availability analysis of selected mining machinery. Arch. Control Sci. 27(2), 197–209 (2017). https://doi.org/10.1515/acsc-2017-0012

    Article  Google Scholar 

  4. Brzychczy, E., Lipiński, P., Zimroz, R., Filipiak, P.: Artificial immune systems for data classification in planetary gearboxes condition monitoring. In: Dalpiaz, G. (ed.) Advances in Condition Monitoring of Machinery in Non-stationary Operations, CMMNO 2013. Lecture Notes in Mechanical Engineering. Springer, Heidelberg (2014)

    Google Scholar 

  5. Cook, D.J., Krishnan, N.C., Rashidi, P.: Activity discovery and activity recognition: a new partnership. IEEE Trans. Cybern. 43(3), 820–828 (2013)

    Article  Google Scholar 

  6. Demetgul, M.: Fault diagnosis on production systems with support vector machine and decision trees algorithms. Int. J. Adv. Manuf. Technol. 67, 2183–2194 (2013). https://doi.org/10.1007/s00170-012-4639-5

    Article  Google Scholar 

  7. Erkayaoğlu, M., Dessureault, S.: Using integrated process data of longwall shearers in data warehouses for performance measurement. Int. J. Oil Gas Coal Technol. 16(3) (2017). https://doi.org/10.1504/ijogct.2017.10007433

    Article  Google Scholar 

  8. Guenther, C.W., van der Aalst, W.M.P.: Mining Activity Clusters from Low-Level Event Logs. BETA Working Paper Series, WP 165, Eindhoven University of Technology, Eindhoven (2006)

    Google Scholar 

  9. Korbicz, J., Koscielny, J.M., Kowalczuk, Z., Cholewa, W. (eds.): Fault Diagnosis: Models, Artificial Intelligence, Applications. Springer (2004)

    Google Scholar 

  10. Le, T., Luo, M., Zhou, J., Chan, H.L.: Predictive maintenance decision using statistical linear regression and kernel methods. In: Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA), Barcelona, pp. 1–6 (2014). https://doi.org/10.1109/etfa.2014.7005357

  11. Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P., Toussaint, P.J.: From low-level events to activities - a pattern-based approach. In: International Conference on Business Process Management, pp. 125–141. Springer (2016)

    Google Scholar 

  12. Michalak, M., Sikora, B., Sobczyk, J.: Correlation and association analysis in wall conveyor engines diagnosis. Studia Informatica [S.l.] 36(3), 43–60 (2015). https://doi.org/10.21936/si2015_v36.n3.737

    Article  Google Scholar 

  13. Neustupa, Z., Benes, F., Kebo, V., Kodym, O.: New trends of automation and control of opencast mining technology. In: 13th SGEM GeoConference on Science and Technologies in Geology, Exploration and Mining, vol. 1, pp. 555–562 (2013). https://doi.org/10.5593/sgem2013/ba1.v1/s03.044

  14. Reid, P.B., Dunn, M.T., Reid, D.C., Ralston, J.C.: Real-world automation: new capabilities for underground longwall mining. In: Proceedings of the Australasian Conference on Robotics and Automation Brisbane, Australia (2010)

    Google Scholar 

  15. Sammouri, W.: Data mining of temporal sequences for the prediction of infrequent failure events: application on floating train data for predictive maintenance. Signal and Image processing. Université Paris-Est (2014)

    Google Scholar 

  16. Singh, R.D.: Principles and Practices of Modern Coal Mining. New Age International, New Delhi (2005)

    Google Scholar 

  17. Snopkowski, R., Napieraj, A., Sukiennik, M.: Method of the assessment of the influence of longwall effective working time onto obtained mining output. Arch. Min. Sci. 61(4), 967–977 (2016). https://doi.org/10.1515/amsc-2016-0064

    Article  Google Scholar 

  18. Stecula, K., Brodny, J.: Application of the OEE Model to analyse the availability of the mining armored face conveyor. In: Conference: 16th International Multidisciplinary Scientific Geoconference (SGEM 2016). Exploration and Mining, Albena, Bulgaria, vol. II, pp. 57–64 (2016)

    Google Scholar 

  19. Susto, G.A., Beghi, A.: Dealing with time-series data in predictive maintenance problems. In: IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), Berlin, pp. 1–4 (2016). https://doi.org/10.1109/etfa.2016.7733659

  20. Sztyler, T., Carmona, J., Völker, J., Stuckenschmidt, H.: Self-tracking reloaded: applying process mining to personalized health care from labeled sensor data. In: Koutny, M., Desel, J., Kleijn, J. (eds.) Transactions on Petri Nets and Other Models of Concurrency XI. LNCS, vol. 9930, pp. 168–180. Springer, Heidelberg (2016)

    Chapter  Google Scholar 

  21. Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.P.: Event abstraction for process mining using supervised learning techniques. In: Bi, Y., Kapoor, S., Bhatia, R. (eds.) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15, pp. 251–269. Springer, Cham (2018)

    Google Scholar 

  22. van der Aalst, W.M.P.: Process Mining in the Large: A Tutorial. In: Zimanyi, E. (ed.) Business Intelligence (eBISS 2013). Lecture Notes in Business Information Processing, vol. 172, pp. 33–76. Springer, Berlin (2014)

    Chapter  Google Scholar 

  23. van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer, Berlin (2016)

    Book  Google Scholar 

  24. van der Aalst, W.M.P., Bolt, A., van Zelst, S.J.: RapidProM: mine your processes and not just your data. In: Hofmann, M., Klinkenberg, R. (eds.) RapidMiner: Data Mining Use Cases and Business Analytics Applications. Chapman and Hall/CRC Press (2016)

    Google Scholar 

  25. van Eck, M.L., Sidorova, N., van der Aalst, W.M.P.: Enabling process mining on sensor data from smart products. In: IEEE RCIS, pp. 1–12. IEEE Computer Society Press, Brussels (2016) https://doi.org/10.1109/rcis.2016.7549355

  26. Widodo, A., Yang, B.S., Han, T.: Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert Syst. Appl. 32, 299–312 (2007)

    Article  Google Scholar 

  27. Zorychta, A., Burtan, Z.: Conditions and future directions for technological developments in the coal mining sector. Min. Resour. Manage. 24(1), 53–70 (2008)

    Google Scholar 

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Acknowledgements

This paper presents the results of research conducted at AGH University of Science and Technology – contract no 11.11.100.693.

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Correspondence to Edyta Brzychczy .

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Brzychczy, E., Trzcionkowska, A. (2019). Process-Oriented Approach for Analysis of Sensor Data from Longwall Monitoring System. In: Burduk, A., Chlebus, E., Nowakowski, T., Tubis, A. (eds) Intelligent Systems in Production Engineering and Maintenance. ISPEM 2018. Advances in Intelligent Systems and Computing, vol 835. Springer, Cham. https://doi.org/10.1007/978-3-319-97490-3_58

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