Advertisement

Anomaly Detection and Structural Analysis in Industrial Production Environments

  • Martin AtzmuellerEmail author
  • David Arnu
  • Andreas Schmidt
Conference paper

Zusammenfassung

Das Erkennen von anormalem Verhalten kann im Kontext industrieller Anwendung von entscheidender Bedeutung sein. Während moderne Produktionsanlagen mit hochentwickelten Alarmsteuerungssytemen ausgestattet sind, reagieren diese hauptsächlich auf Einzelereignisse. Aufgrund der großen Anzahl und der verschiedenen Arten von Datenquellen ist ein einheitlicher Ansatz zur Anomalieerkennung nicht immer möglich. Eine weitverbreitete Datenart sind Logeinträge von Alarmmeldungen. Sie erlauben im Vergleich zu Sensorrohdaten einen höheren Abstraktionsgrad. In einem industriellen Produktionsszenario verwenden wir sequentielle Alarmdaten zur Anomalieerkennung und -auswertung, basierend auf erstrangigen Markov-Kettenmodellen. Wir umreißen hypothesegetriebene und beschreibungsorientierte Modellierungsoptionen. Außerdem stellen wir ein interaktives Dashboard zur Verfügung, um die Ergebnisse zu untersuchen und darzustellen.

Schlüsselwörter

anomaly detection exceptional model mining sequence mining sequential patterns industry 4.0 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. [1] Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin (2011)Google Scholar
  2. [2] Abele, L., Anic, M., Gutmann, T., Folmer, J., Kleinsteuber, M., Vogel-Heuser, B.: Combining Knowledge Modeling and Machine Learning for Alarm Root Cause Analysis. In: Proc. IFAC Volumes, 46(9):1843–1848. International Federation of Automatic Control (2013)Google Scholar
  3. [3] Akoglu, L., Tong, H., Koutra, D.: Graph Based Anomaly Detection and Description. Data Min Knowl Disc 29(3), 626–688 (May 2015)Google Scholar
  4. [4] Amer, M., Goldstein, M.: Nearest-Neighbor and Clustering-based Anomaly Detection Algorithms for Rapidminer. In: Proc. of the 3rd RapidMiner Community Meeting and Conference (RCOMM 2012). pp. 1–12 (2012)Google Scholar
  5. [5] Atzmueller, M.: Analyzing and Grounding Social Interaction in Online and Offline Networks. In: Proc. ECML PKDD. LNCS, vol. 8726, pp. 485–488. Springer, Heidelberg, Germany (2014)Google Scholar
  6. [6] Atzmueller, M.: Data Mining on Social Interaction Networks. Journal of Data Mining and Digital Humanities 1 (June 2014)Google Scholar
  7. [7] Atzmueller, M.: Subgroup Discovery - Advanced Review. WIREs: Data Mining and Knowledge Discovery, (5)1:35–49 (2015)Google Scholar
  8. [8] Atzmueller, M.: Detecting Community Patterns Capturing Exceptional Link Trails. Proc. IEEE/ACM ASONAM, IEEE Press, Boston, MA, USA (2016)Google Scholar
  9. [9] Atzmueller, M.: Local Exceptionality Detection on Social Interaction Networks. In: Proc. ECML PKDD 2016: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Springer, Heidelberg, Germany (2016)Google Scholar
  10. [10] Atzmueller, M., Baumeister, J., Puppe, F.: Introspective Subgroup Analysis for Interactive Knowledge Refinement. In: Proc. FLAIRS Conference, pp. 402-407, AAAI Press, Palo Alto, CA, USA (2006)Google Scholar
  11. [11] Atzmueller, M., Doerfel, S., Mitzlaff, F.: Description-Oriented Community Detection using Exhaustive Subgroup Discovery. Information Sciences, 329, 965-984. (2016)Google Scholar
  12. [12] Atzmueller, M., Doerfel, S., Hotho, A., Mitzlaff, F., Stumme, G.: Faceto-Face Contacts at a Conference: Dynamics of Communities and Roles. In: Modeling and Mining Ubiquitous Social Media, LNAI, vol. 7472. Springer, Heidelberg, Germany (2012)Google Scholar
  13. [13] Atzmueller, M., Mollenhauer, D., Schmidt, A.: Big Data Analytics Using Local Exceptionality Detection. In: Enterprise Big Data Engineering, Analytics, and Management, IGI Global, Hershey, PA, USA, 2016.Google Scholar
  14. [14] Atzmueller, M., Kloepper, B., Mawla, H.A., Jäschke, B., Hollender, M., Graube, M., Arnu, D., Schmidt, A., Heinze, S., Schorer, L., Kroll, A., Stumme, G., Urbas, L.: Big Data Analytics for Proactive Industrial Decision Support: Approaches First Experiences in the Context of the FEE Project. atp edition 58(9):62-74 (2016)Google Scholar
  15. [15] Atzmueller, M., Roth-Berghofer, T.: The Mining and Analysis Continuum of Explaining Uncovered. Proc. 30th SGAI International Conference on Artificial Intelligence (2010)Google Scholar
  16. [16] Atzmueller M, Schmidt A, Kibanov M. DASHTrails: An Approach for Modeling and Analysis of Distribution-Adapted Sequential Hypotheses and Trails. In: Proc. WWW 2016 (Companion). IW3C2 / ACM, New York, NY, USA (2016)Google Scholar
  17. [17] Atzmueller M., Schmidt A., Kloepper B., Arnu D.: HypGraphs: An Approach for Modeling and Comparing Graph-Based and Sequential Hypotheses. In: Proc. ECML PKDD Workshop on New Frontiers in Mining Complex Patterns (NFMCP). Riva del Garda, Italy (2016).Google Scholar
  18. [18] Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: OPTICS-OF: Identifying Local Outliers, pp. 262–270. Springer, Berlin/Heidelberg (1999)Google Scholar
  19. [19] Dean, J., Ghemawat, S.: Mapreduce: Simplified Data Processing on Large Clusters. Communications of the ACM 51(1), 107–113 (2008).Google Scholar
  20. [20] Folmer, J., Kirchen, I., Trunzer, E., Vogel-Heuser, B., Pötter, T., Graube, M., Heinze, S., Urbas, L., Atzmueller, M., Arnu, D: Challenges for Big and Smart Data in Process Industries. atp edition, 01/02 (2017)Google Scholar
  21. [21] Folmer, J., Schuricht, F., Vogel-Heuser, B.: Detection of Remporal Dependencies in Alarm Time Series of Industrial Plants. Proc. IFAC, pp. 24–29, International Federation of Automatic Control (2014)Google Scholar
  22. [22] Hawkins,D.: Identification of Outliers. Chapman and Hall, London, UK (1980)Google Scholar
  23. [23] Kibanov, M., Atzmueller, M., Scholz, C., Stumme, G.: Temporal Evolution of Contacts and Communities in Networks of Face-to-Face Human Interactions. Science China Information Sciences 57 (2014)Google Scholar
  24. [24] Klöpper, B., Dix, M., Schorer, L, Ampofo, A., Atzmueller, M., Arnu, D., Klinkenberg, R.: Defining Software Architectures for Big Data Enabled Operator Support Systems. In: Proc. INDIN. IEEE Press, Boston, MA, USA (2016)Google Scholar
  25. [25] Leman, D., Feelders, A., Knobbe, A.: Exceptional Model Mining. In: Proc. ECML PKDD, pp. 1-16, Springer, Heidelberg, Germany (2008)Google Scholar
  26. [26] Lemmerich, M., Atzmueller, M., Puppe, F.: Fast Exhaustive Subgroup Discovery with Numerical Target Concepts. Data Mining and Knowledge Discovery, (30):711-762 (2016)Google Scholar
  27. [27] Lemmerich, M., Becker, M., Atzmueller, M.: Generic Pattern Trees for Exhaustive Exceptional Model Mining. Proc. ECML PKDD 2012, pp. 277-292, Springer, Heidelberg, Germany (2012)Google Scholar
  28. [28] Lempel, R., Moran, S.: The Stochastic Approach for Link-Structure Analysis (SALSA) and the TKC Effect. Computer Networks 33(1), 387–401 (2000)Google Scholar
  29. [29] Macek, B.E., Scholz, C., Atzmueller, M., Stumme, G.: Anatomy of a Conference. In: Proc. ACM Hypertext. pp. 245–254. ACM Press, New York, NY, USA (2012)Google Scholar
  30. [30] Martí, L., Sanchez-Pi, N., Molina, J.M., Garcia, A.C.B.: Anomaly Detection based on Sensor Data in Petroleum Industry Applications. Sensors 15(2), 2774–2797 (2015)Google Scholar
  31. [31] Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: Yale: Rapid Prototyping for Complex Data Mining Tasks. In: Proc. KDD. pp. 935–940. ACM, New York, NY, USA (2006)Google Scholar
  32. [32] Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: Community Assessment using Evidence Networks. In: Analysis of Social Media and Ubiquitous Data. LNAI, vol. 6904 (2011)Google Scholar
  33. [33] Mitzlaff, F., Atzmueller, M., Hotho, A., Stumme, G.: The Social Distributional Hypothesis. SNAM 4(216) (2014)Google Scholar
  34. [34] Munoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Single-Entry Single-Exit Decomposed Conformance Checking. Inf. Syst. 46, 102–122 (2014)Google Scholar
  35. [35] Pirolli, P.L., Pitkow, J.E.:Distribution of Surfers’ Paths through the World Wide Web: Empirical Characterizations. WWW 2(1-2) (1999)Google Scholar
  36. [36] Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C., Samatova, N.F.: Anomaly Detection in Dynamic Networks: A Survey. WIREs: Comput. Statistics 7(3), 223–247 (2015)Google Scholar
  37. [37] Rozinat, A., Aalst, W.: Conformance Checking of Processes Based on Monitoring Real Behavior. Information Systems 33(1), 64–95 (2008)Google Scholar
  38. [38] Seipel, D., Köhler, S., Neubeck, P., Atzmueller, M.: Mining Complex Event Patterns in Computer Networks. In: New Frontiers in Mining Complex Patterns (NFMCP), Springer, Heidelberg, Germany (2013)Google Scholar
  39. [39] Singer, P., Helic, D., Hotho, A., Strohmaier, M.: Hyptrails: A Bayesian Approach for Comparing Hypotheses about Human Trails. In: Proc. WWW. ACM, New York, NY, USA (2015)Google Scholar
  40. [40] Singer, P., Helic, D., Taraghi, B., Strohmaier, M.:Memory and Structure in Human Navigation Patterns. PLoS ONE 9(7) (2014)Google Scholar
  41. [41] Strelioff, C.C., Crutchfield, J.P., Hübler, A.W.: Inferring Markov Chains: Bayesian Estimation, Model Comparison, Entropy Rate, and Out-of-Class Modeling. Physical Review E 76(1), 011106 (2007)Google Scholar
  42. [42] Vogel-Heuser, B., Schütz, D., Folmer, J.: Criteria-based alarm flood pattern recognition using historical data from automated production systems (aps). Mechatronics 31, 89–100Google Scholar
  43. [43] Weiss, C. H., Atzmueller, M.: EWMA Control Charts for Monitoring Binary Processes with Applications to Medical Diagnosis Data. Qual. Reliab. Engng. Int., 26: 795–805 (2010)Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH 2017

Authors and Affiliations

  • Martin Atzmueller
    • 1
    Email author
  • David Arnu
    • 2
  • Andreas Schmidt
    • 3
  1. 1.Jheronimus Academy of Data ScienceTilburg University (TiCC), University of Kassel (ITeG)KasselDeutschland
  2. 2.RapidMiner GmbHDortmundDeutschland
  3. 3.University of Kassel (ITeG)KasselDeutschland

Personalised recommendations