Advertisement

Proactive and Predictive Maintenance of Cyber-Physical Systems

  • Maxim V. ShcherbakovEmail author
  • Artem V. Glotov
  • Sergey V. Cheremisinov
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 259)

Abstract

The following chapter describes a concept model for proactive decision support system based on (real-time) predictive analytics and designed for maintenance of cyber-physical systems (CPSs) in order to optimize its downtime. This concept later is referred to as proactive and predictive maintenance decision support systems or P2M for short. The concept is based on (i) the axioms of predictive decisions making, (ii) the proactive computing principles and (iii) models and methods for intelligent data processing. The aforementioned concept extends an idea of data-driven intelligent systems by using two approaches. The first approach implements predictive analytics, i.e. detection of a pre-failure event (called a proactive event) over a certain time period. This approach is based on the sequence of the following operational processes: to detect–to predict–to decide–to act. The second approach helps to automate maintenance decisions, which allows to exclude operational roles and move to supervisory level positions in the operational management structure. The concept includes the following primary components: ontology, a data warehouse (data lake), data factory as a set of data processing methods, flexible pipelines for data handling and processing and business processes with predictive decision logic for cyber-physical systems maintenance. This concept model is considered as the platform for the design of cyber-physical asset performance management systems.

Keywords

Industrial cyber-physical systems Proactive decision support Predictive maintenance 

Notes

Acknowledgements

This research was supported by the Russian Fund of Basic Research (grant No. 19-47-340010). Special thanks go to George Sergeev for fruitful discussion and essential remarks.

References

  1. 1.
    Gorjian, N., Ma, L., Mittinty, M., Yarlagadda, P., Sun, Y.: A review on degradation models in reliability analysis. In: Kiritsis, D., Emmanouilidis, C., Koronios, A., Mathew, J. (eds.) Engineering Asset Lifecycle Management, pp. 369–384. Springer, London (2010)CrossRefGoogle Scholar
  2. 2.
    Tennenhouse, D.: Proactive computing. Commun. ACM 43(5), 43–50 (2000).  https://doi.org/10.1145/332833.332837CrossRefGoogle Scholar
  3. 3.
    Industry 4.0 challenges and solutions for digital transformation and use of exponential technologies. Deloitte. https://www2.deloitte.com/content/dam/Deloitte/ch/Documents/manufacturing/ch-en-manufacturing-industry-4-0-24102014.pdf
  4. 4.
    Industry 4.0—opportunities and challenges of Industrial Internet. PricewaterhouseCoopers. https://www.pwc.nl/en/assets/documents/pwc-industrie-4-0.pdf
  5. 5.
    Tupa, J., Jan, S., Steiner, F.: Aspects of risk management implementation for industry 4.0, Procedia Manufacturing, vol. 11, pp. 1223–1230 (2017). ISSN 2351-9789.  https://doi.org/10.1016/j.promfg.2017.07.248CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Canizo, M., Onieva, E., Conde, A., Charramendieta, S., Trujillo, S.: Real-Time Predictive Maintenance for Wind Turbines Using Big Data Frameworks, pp. 1–8 (n.d.)Google Scholar
  8. 8.
    Ciomek, K., Ferretti, V., Ferretti, V.: Predictive analytics and disused railways requalification: insights from a post factum analysis perspective. Decis. Support Syst. (2017).  https://doi.org/10.1016/j.dss.2017.10.010CrossRefGoogle Scholar
  9. 9.
    Cleary, D., Tax, R.I.: Predictive analytics in the public sector: using data mining to assist better target selection for audit. Electron. J. E-Gov.Ment 9(2), 132–140 (2011)Google Scholar
  10. 10.
    Yu, L.: Wind turbine data analytics for drive-train failure early detection and diagnostics. Volume 1: Aircraft Engine; Ceramics; Coal, Biomass and Alternative Fuels; Wind Turbine Technology, pp. 721–728 (2011).  https://doi.org/10.1115/GT2011-45101
  11. 11.
    Schmidhuber, J.: Deep Learning in Neural Networks: An Overview. https://arxiv.org/abs/1404.7828
  12. 12.
    Chen, T., Guestrin, C.: XGBoost: A Scalable Tree Boosting System. https://arxiv.org/abs/1603.02754
  13. 13.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org
  14. 14.
    Deng, L., Yu D.: Deep Learning: Methods and Applications. Microsoft Research. One Microsoft Way. Redmond, WA 98052Google Scholar
  15. 15.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015).  https://doi.org/10.1038/nature14539CrossRefGoogle Scholar
  16. 16.
    Dehghani, M., et al.: Universal Transformers. https://arxiv.org/pdf/1807.03819.pdf
  17. 17.
    Bai, S., Kolter, J.Z., Koltun, V.: An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. https://arxiv.org/pdf/1803.01271
  18. 18.
    Elbayad, M., Besacier, L., Verbeek, J.: Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction. https://arxiv.org/pdf/1808.03867
  19. 19.
    Pons, M.B.: The expected value of perfect information in unrepeatable decision-making. Decis. Support Syst. (2018).  https://doi.org/10.1016/j.dss.2018.03.003CrossRefGoogle Scholar
  20. 20.
    Arnott, D., Pervan, G.: Eight key issues for the decision support systems discipline. Decis. Support Syst. 44(3), 657–672 (2008).  https://doi.org/10.1016/j.dss.2007.09.003CrossRefGoogle Scholar
  21. 21.
    Arnott, D., Pervan, G.: A critical analysis of decision support systems research revisited: the rise of design science. J. Inf. Technol. 29(4), 269–293 (2014).  https://doi.org/10.1057/jit.2014.16CrossRefGoogle Scholar
  22. 22.
    VanSyckel, S., Becker, C.: A survey of pervasive computing. UBICOMP Comput. Sci. 3(6), 1–7 (2014).  https://doi.org/10.1145/2638728.2641672CrossRefGoogle Scholar
  23. 23.
    ISO/IEC/IEEE 15288: Systems and Software Engineering—System Life Cycle Processes (2015). https://www.iso.org/ru/standard/63711.html
  24. 24.
    Nural, M.V., Cotterell, M.E., Peng, H., Xie, R., Ma, P., Miller, J.A.: Automated predictive big data analytics using ontology based semantics. Int. J. Big Data 2(2), 43–56 (2015). https://www.ncbi.nlm.nih.gov/pmc/chapters/PMC5898823/
  25. 25.
    Jarvenpaa, E., Siltala, N., Hylli, O., Minna, L.: The development of an ontology for describing the capabilities of manufacturing resources. J. Intell. Manuf. 30, 959 (2019).  https://doi.org/10.1007/s10845-018-1427-6CrossRefGoogle Scholar
  26. 26.
    Anufriev, D., Petrova, I., Kravets, A., Vasiliev, S.: Big data-driven control technology for the heterarchic system (building cluster case-study). Stud. Syst. Decis. Control. 181, 205–222 (2019)CrossRefGoogle Scholar
  27. 27.
    Sure, Y., Staab, S., Studer, R.: Ontology engineering methodology. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, 2nd edn., pp. 135–152. Springer, New York (2009). ISBN 978-3-540-70999-2CrossRefGoogle Scholar
  28. 28.
    Kizim A., Matokhina, A., Nesterov, B.: Development of ontological knowledge representation model of industrial equipment. Creativity in Intelligent Technologies and Data Science, CIT&DS 2015, Volgograd, Russia, 15–17 Sep 2015. Proceedings, vol. 535, pp. 354. Springer (2015)Google Scholar
  29. 29.
    Tran, V.P., Shcherbakov, M., Nguyen, T.A.: Yet another method for heterogeneous data fusion and preprocessing in proactive decision support systems: distributed architecture approach. In: Vishnevskiy V., Samouylov K., Kozyrev D. (eds.) Distributed Computer and Communication Networks. DCCN 2017. Communications in Computer and Information Science, vol. 700. Springer, Cham (2017)Google Scholar
  30. 30.
    Kizim, A.V., et al.: Predictive modeling as a basis for monitoring, diagnosis, forecasting and upgrading of a technical system. In: 2017 IEEE 11th International Conference on Application of Information and Communication Technologies (AICT). IEEE, pp. 1–5 (2017)Google Scholar
  31. 31.
    Golubev, A., Shcherbakov, M., Shcherbakova, N., Kamaev, V.: Automatic multi-steps forecasting method for multi seasonal time series based on symbolic aggregate approximation and grid search approaches. J. Fundam. Appl. Sci. 8(3S), 2529–2541 (2016)Google Scholar
  32. 32.
    Shcherbakov, M.V., Brebels A., Shcherbakova, N.L., Kamaev, V.A., Gerget, O.M., Devyatuch, D.V.: Outlier detection and classification in sensor data streams for proactive decision support systems. In: International Conference on Information Technologies in Business and Industry 2016, Tomsk, Rus. Federation, 21–23 Sep 2016. Chapter № 012143, J. Phys Conf. Ser. 803(1), 8 (2017)Google Scholar
  33. 33.
    Shcherbakov, M.V., Groumpos, P.P., Kravets A.G.: A method and IR4I index indicating the readiness of business processes for data science solutions. Creativity in Intelligent Technologies and Data Science. Second Conference, CIT&DS 2017 (Volgograd, Russia, September 12–14, 2017). In: Kravets, A., Shcherbakov, M., Kultsova, M., Peter Groumpos; Volgograd State Technical University et al. (eds.) Proceedings. Springer International Publishing AG, Germany, 2017, vol. 754, pp. 21–34. (Ser. Communications in Computer and Information Science)Google Scholar
  34. 34.
    Kravets, A., Kozunova, S.: The risk management model of design department’s PDM information system. Commun. Comput. Inf. Sci. 754, 490–500 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Volgograd State Technical UniversityVolgogradRussia
  2. 2.Mobile Gas Turbine Energy Stations JSC CompanyMoscowRussia

Personalised recommendations