Advertisement

Outlier Detection in Predictive Analytics for Energy Equipment

  • Alexander AndryushinEmail author
  • Ivan Shcherbatov
  • Nina Dolbikova
  • Anna Kuznetsova
  • Grigory Tsurikov
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 259)

Abstract

The method of data preprocessing used to predict the technical condition of power equipment is described. Preprocessing implemented using neural networks allows us to identify and eliminate outliers in the investigated data. An example illustrating the proposed method of processing big data using bagged trees algorithm, support vector machines and artificial neural networks is shown.

Keywords

Predictive analytics system Energy Data preprocessing Neural network Bagged trees Support vector machines 

Notes

Acknowledgements

This work is supported by the Russian Science Foundation under grant № 19-19-00601.

References

  1. 1.
    Protalinsky, O., Khanova, A., Shcherbatov, I. Simulation of power assets management process. In: Studies in Systems, Decision and Control (2019)Google Scholar
  2. 2.
    Protalinsky, O.M., Shcherbatov, I.A., Stepanov, P.V.: Identification of the actual state and entity availability forecasting in power engineering using neural-network technologies. J Phys Conf Ser (2017)Google Scholar
  3. 3.
    Wang J., Zhang W., Shi Y., Duan S., Liu J.: Industrial big data analytics: challenges, methodologies, and applications. https://arxiv.org/ftp/arxiv/papers/1807/1807.01016.pdf. Accessed 16 Mar 2019
  4. 4.
    Bi, Z.M., Cochran, D.S.: Big data analytics with applications. J. Manag. Anal. 1(4), 249–265 (2014)Google Scholar
  5. 5.
    Kumaraguru S., Kulvatunyou B., Morris K.C.: Integrating real-time analytics and continuous performance management in smart manufacturing systems. In: Proceedings of the IFIP Advances in Information and Communication Technology, pp. 175–182 (2014)CrossRefGoogle Scholar
  6. 6.
    Chidambaram, V., Evans, H., Etheredge, K.: Big data: is the energy industry starting to see real applications? Supply Chain Manag. Rev. 12, 62–64 (2015)Google Scholar
  7. 7.
    Silipo, R., Winters, P.: Big data, smart energy, and predictive analytics time series prediction of smart energy data. https://files.knime.com/sites/default/files/inline-images/knime_bigdata_energy_timeseries_whitepaper.pdf. Accessed 18 Mar 2019
  8. 8.
    Lu, Y.: Industry 4.0: a survey on technologies, applications and open research issues. J. Ind. Inf. Integ. 6, 1–10 (2017)CrossRefGoogle Scholar
  9. 9.
    Shin, S.J., Meilanitasari, P.: Developing a big data analytics platform for manufacturing systems: architecture, method, and implementation. International J. Adv. Manuf. Technol., 1–42 (2018)Google Scholar
  10. 10.
    Peres, R.S., Rocha, A.D., Leitao, P., Barata, J.: IDARTS—towards intelligent data analysis and real-time supervision for Industry 4.0. Comput. Ind., 1–12 (2018)Google Scholar
  11. 11.
    Wang, J., Ma, Y., Zhang, L., Gao, R.X., Wu, D.: Deep learning for smart manufacturing: methods and applications. J. Manuf. Syst. 48, 144–156 (2018)CrossRefGoogle Scholar
  12. 12.
    Rose, K., Eldridge, S., Chapin, L.: The internet of things: an overview. Internet Soc., 7 (2015)Google Scholar
  13. 13.
    Shina, S.J., Wooa, J., Rachuri, S.: Predictive analytics model for power consumption in manufacturing. In: Proceedings in 21st CIRP Conference on Life Cycle Engineering, pp. 153–158 (2014)CrossRefGoogle Scholar
  14. 14.
    Park, J.K., Kwon, B.K., Park, J.H., Kang, D.J.: Machine learning-based imaging system for surface defect inspection. Int. J. Precis. Eng. Manuf.-Green Technol. 3(3), 303–310 (2016)CrossRefGoogle Scholar
  15. 15.
    Zhao, R., Yan, R., Chen, Z., Chen, Z., Mao, K., Wang, P., et al.: Deep learning and its applications to machine health monitoring: a survey. https://arxiv.org/pdf/1612.07640.pdf. Accessed 2 Apr 2019
  16. 16.
    Johnson, T., Kwok, I., Ng, R.T.: Fast computation of 2-dimensional depth contours. In: Proceedings of the ACM KDD Conference, pp. 224–228, New York, NY, USA, 27–31 Aug 1998Google Scholar
  17. 17.
    Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104, Dallas, TX, USA, 16–18 May 2000. ACM, New York, NY, USA (2000)Google Scholar
  18. 18.
    Marti, L., Sanchez-Pi, N., Molina, J.M., Bicharra Garcia, A.C.: Anomaly detection based on sensor data in petroleum industry applications. Sensors 15, 2774–2797 (2015)CrossRefGoogle Scholar
  19. 19.
    Loureiro, A., Torgo, L., Soares, C.: Outlier detection using clustering methods: a data cleaning application. In: Malerba, D., May, M. (eds.) Proceedings of KDNet Symposium on Knowledge-based Systems for the Public Sector (2004)Google Scholar
  20. 20.
    Upadhyaya, S., Singh, K.: Classification based outlier detection techniques. Int. J. Comput. Trends Technol. 3(2), 294–298 (2012)Google Scholar
  21. 21.
    Ting, K.M.: An instance-weighting method to induce cost-sensitive trees. IEEE Trans. Knowl. Data Eng. 14, 659–665 (2002)CrossRefGoogle Scholar
  22. 22.
    Weiss, G., Provost, F.: Learning when training data are costly: the effect of class distribution on tree induction. J. Artif. Intell. Res. 19, 315–354 (2003)CrossRefGoogle Scholar
  23. 23.
    Tang, Y., Zhang, Y.Q., Chawla, N.V., Krasser, S.: SVMs modeling for highly imbalanced classification. IEEE Trans. Syst. Man Cybern. B Cybern. 39(1), 281–288 (2009)CrossRefGoogle Scholar
  24. 24.
    Wu, G., Chang, E.Y.: Class-boundary alignment for imbalanced dataset learning. In: Proceedings of the ICML Workshop on Learning from Imbalanced Data Sets (2003)Google Scholar
  25. 25.
    Fedorov, E.E.: Artificial neural networks: monograph, 317 p. DVNZ DonNTU, Krasnoarmeysk (2016) (in Russian)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alexander Andryushin
    • 1
    Email author
  • Ivan Shcherbatov
    • 1
  • Nina Dolbikova
    • 1
  • Anna Kuznetsova
    • 1
  • Grigory Tsurikov
    • 1
  1. 1.Moscow Power Engineering InstituteMoscowRussia

Personalised recommendations