Validation of similarity measures for industrial alarm flood analysis?

Open Access
Part of the Technologien für die intelligente Automation book series (TIA, volume 8)


The aim of industrial alarm flood analysis is to assist plant operators who face large amounts of alarms, referred to as alarm floods, in their daily work. Many methods used to this end involve some sort of a similarity measure to detect similar alarm sequences. However, multiple similarity measures exist and it is not clear which one is best suited for alarm analysis. In this paper, we perform an analysis of the behaviour of the similarity measures and attempt to validate the results in a semi-formalised way. To do that, we employ synthetically generated floods, based on assumption that synthetic floods that are generated as ’similar’ to the original floods should receive similarity scores close to the original floods. Consequently, synthetic floods generated as ’not-similar’ to the original floods are expected to receive different similarity scores. Validation of similarity measures is performed by comparing the result of clustering the original and synthetic alarm floods. This comparison is performed with standard clustering validation measures and application-specific measures.


  1. 1.
    Ahmed, K., Izadi, I., Chen, T., Joe, D., Burton, T.: Similarity analysis of industrial alarm flood data. In: IEEE Transactions on Automation Science and Engineering (Apr 2013)Google Scholar
  2. 2.
    Bergquist, T., Ahnlund, J., Larsson, J.E.: Alarm reduction in industrial process control. In: Proc. IEEE Conference on Emerging Technologies and Factory Automation. vol. 2, pp. 58–65 (2003)Google Scholar
  3. 3.
    Charbonnier, S., Bouchair, N., Gayet, P.: A weighted dissimilarity index to isolate faults during alarm floods. Control Engineering Practice 45, 110–122 (2015)Google Scholar
  4. 4.
    Charbonnier, S., Bouchair, N., Gayet, P.: Fault template extraction to assist operators during industrial alarm floods. Engineering Applications of Artificial Intelligence 50, 32–44 (2016)Google Scholar
  5. 5.
    Deza, M.M., Deza, E.: Encyclopedia of Distances. Springer (2009)Google Scholar
  6. 6.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD. pp. 226–231. AAAI Press (1996)Google Scholar
  7. 7.
    Fullen, M., Schüller, P., Niggemann, O.: Defining and validating similarity measures for industrial alarm flood analysis. In: IEEE 15th International Conference on Industrial Informatics (INDIN) (2017)Google Scholar
  8. 8.
    Fullen, M., Schüller, P., Niggemann, O.: Semi-supervised case-based reasoning approach to alarm flood analysis. In: Machine Learning for Cyber Physical Systems (ML4CPS) (2017)Google Scholar
  9. 9.
    Instrumentation, Systems, and Automation Society: ANSI/ISA-18.2-2009: Management of Alarm Systems for the Process Industries (2009)Google Scholar
  10. 10.
    Izadi, I., Shah, S.L., Shook, D.S., Chen, T.: An introduction to alarm analysis and design. In: IFAC SAFEPROCESS. pp. 645–650 (2009)Google Scholar
  11. 11.
    Jaccard, P.: Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 241–272 (1901)Google Scholar
  12. 12.
    Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 28, 11–21 (1972)Google Scholar
  13. 13.
    Laberge, J.C., Bullemer, P., Tolsma, M., Reising, D.V.C.: Addressing alarm flood situations in the process industries through alarm summary display design and alarm response strategy. Intl. J. of Industrial Ergonomics 44(3), 395–406 (2014)Google Scholar
  14. 14.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 10(8), 707–710 (1966)Google Scholar
  15. 15.
    Niggemann, O., Lohweg, V.: On the diagnosis of cyber-physical production systems: State-of-the-art and research agenda. In: Proc. AAAI. pp. 4119–4126. AAAI Press (2015)Google Scholar
  16. 16.
    Norwegian Petroleum Directorate: YA-711 Principles for alarm system design (2001)Google Scholar
  17. 17.
    Rand, W.M.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical association 66(336), 846–850 (1971)Google Scholar
  18. 18.
    Vogel-Heuser, B., Schütz, D., Folmer, J.: Criteria-based alarm flood pattern recognition using historical data from automated production systems (aps). Mechatronics 31, 89–100 (2015)Google Scholar
  19. 19.
    Wang, J., Yang, F., Chen, T., Shah, S.L.: An overview of industrial alarm systems: Main causes for alarm overloading, research status, and open problems. IEEE Transactions on Automation Science and Engineering 13(2), 1045–1061 (2016)Google Scholar
  20. 20.
    Wang, J., Li, H., Huang, J., Su, C.: A data similarity based analysis to consequential alarms of industrial processes. Journal of Loss Prevention in the Process Industries 35, 29–34 (2015)Google Scholar
  21. 21.
    Yang, F., Shah, S., Xiao, D., Chen, T.: Improved correlation analysis and visualization of industrial alarm data. ISA Transactions 51(4), 499–506 (2012)Google Scholar

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Authors and Affiliations

  1. 1.Fraunhofer IOSB-INA Institutsteil für industrielle AutomationLemgoGermany
  2. 2.Technische Universität Wien, Institut für Logic and ComputationViennaAustria
  3. 3.Institute Industrial ITLemgoGermany

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