Machine Learning Based Predictive Maintenance of Infrastructure Facilities in the Cryolithozone

  • Andrey V. TimofeevEmail author
  • Viktor M. Denisov
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 255)


This chapter provides some practical aspects and peculiarities of the use of Machine Learning based Predictive Maintenance for the infrastructure facilities in the cryolithozone. Some mathematical models of Machine Learning based Predictive Maintenance are described, which have shown their practical effectiveness. The solutions of several important problems of Predictive Maintenance for pipelines located in cryolithozone are considered, including: problem of leak detection from pipelines taking into account the possible damage to the pipeline foundation due melting of permafrost; problem of automatic classifying of defects that led to leaks; problem of prompt corrosion spot detection in the pipelines as well as problem of identifying the current state of the corrosion process in the pipeline. The problem of optimizing the procedure for incident tickets processing in the Predictive Maintenance system for oil pipelines was also considered.


  1. 1.
    Hashemian, H.M., Bean, W.C.: State-of-the-art predictive maintenance techniques. IEEE Trans. Instrum. Meas. 60(10), 3480–3492 (2011). Scholar
  2. 2.
    Carnero, M.C.: Selection of diagnostic techniques and instrumentation in a predictive maintenance program. A case study. Decis. Support Syst. 38(4), 539–555 (2005)CrossRefGoogle Scholar
  3. 3.
    Swanson, D.C.: A general prognostic tracking algorithm for predictive maintenance. In: 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542), Big Sky, MT, USA, vol. 6, pp. 2971–2977. (2001)
  4. 4.
    Zhou, X., Xi, L., Lee, J.: Reliability-centered predictive maintenance scheduling for a continuously monitored system subject to degradation. Reliab. Eng. Syst. Saf. 92(4), 530–534 (2007)CrossRefGoogle Scholar
  5. 5.
    Kaiser, K.A., Gebraeel, N.Z.: Predictive maintenance management using sensor-based degradation models. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 39(4), 840–849 (2009)CrossRefGoogle Scholar
  6. 6.
    Grall, L. Dieulle, C.B., Roussignol, M.: Continuous-time predictive-maintenance scheduling for a deteriorating system. IEEE Trans. Reliab. 51(2), 141–150 (2002).
  7. 7.
  8. 8.
    Cline, B., Niculescu, R.S., Huffman, D., Deckel, B.: Predictive maintenance applications for machine learning. In: 2017 Annual Reliability and Maintainability Symposium (RAMS), Orlando, FL, pp. 1–7 (2017).
  9. 9.
    Butte, S., Prashanth, A.R., Patil, S.: Machine learning based predictive maintenance strategy: a super learning approach with deep neural networks. In: 2018 IEEE Workshop on Microelectronics and Electron Devices (WMED), Boise, ID, pp. 1–5 (2018).
  10. 10.
    Timofeev, A.V., Denisov, V.M.: Multimodal heterogeneous monitoring of super-extended objects: modern view. recent advances in systems safety and security, 06/2016: chapter. In: Volume 62 of the series Studies in Systems, Decision and Control: pp. 97–116. Springer International Publishing, Berlin. ISBN: 978-3-319-32523-1.
  11. 11.
    Anger, C.: Hidden semi-Markov models for predictive maintenance of rotating elements. Technische Universität, Darmstadt (Ph.D. Thesis) (2018)Google Scholar
  12. 12.
    Bredensteiner, E., Bennett, K.: Multicategory classification by support vector machines. Comput. Optim. Appl. 12, 53–79 (1999)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Krishnapuram, B., Shah, M., Smola, A.J., Aggarwal, C.C., Shen, D., Rastogi, R. (eds.) Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13–17. ACM. pp. 785–794 (2016). arXiv:1603.02754.
  14. 14.
    American Society for Testing and Materials E 1211-97Google Scholar
  15. 15.
    European Committee for Standardization E1211-97: Standard practice for leak detection and location using surface-mounted acoustic emission sensorsGoogle Scholar
  16. 16.
    Savina, A.V.: Analysis of the risk of accidents when justifying safe distances from the main pipelines of liquefied petroleum gas to objects with the presence of people. Ph.D. Thesis: 05.26.03. Scientific-Technical Center of Research Industrial Problems Security, Moscow, vol. 121, p. il (2013). RSL OD, 61 14-5/120Google Scholar
  17. 17.
    Safety Guide: Methodical recommendations for the quality risk analysis of accidents in hazardous production facilities of main oil pipelines and main oil products. Approved by Order of the Federal Service for Environmental, Technological and Nuclear Supervision of June 17, 2016 n. 228: (2016)
  18. 18.
    Annual Report on the Activity of the Federal Service on Environmental, Technological and Atomic Supervision in 2014: Federal Service for Ecological, Technological and Nuclear Supervision of the Russian Federation, Moscow. (2015)
  19. 19.
    Timashev, S.A., Bushinskaya, A.V.: Probabilistic methods for predictive maintenance of pipeline systems. In: Proceedings of the Samara Scientific Center of the Russian Academy of Sciences, vol. 12, no. 1–2, pp. 548-555 (2010)Google Scholar
  20. 20.
    Powers, D.M.W.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation). J. Mach. Learn. Technol. 2(1), 37–63 (2011)MathSciNetGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.LLP “EqualiZoom”AstanaKazakhstan
  2. 2.“Flagman Geo” Ltd.Saint-PetersburgRussia

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