Skip to main content
SpringerLink
Log in

Clustering Algorithms in Mining Fans Operating Mode Identification Problem

  • Conference paper
  • First Online:
Advances in Computational Collective Intelligence (ICCCI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1287))

Included in the following conference series:

  • 1188 Accesses

Abstract

Most of the machinery and equipment of the mine infrastructure is controlled by an industrial automation system. In practice, SCADA (Supervisory Control And Data Acquisition) systems very often acquire many operational parameters that have no further analytical use. The variability of recorded signals very often depends on the machine load, as well as organizational and technical aspects. Therefore, SCADA systems can be a practically free source of information used to determine KPI (e.g. performance, energy and diagnostic) for a single object as well as given mining process. For example, the ability to reliably identify different operational modes of the mining industrial fans based on data from SCADA gives a wide range of potential applications. Accurate information on this subject could be used - apart from basic monitoring and reporting needs (e.g. actual work vs. schedule comparison) – also in more complex problems, like power consumption predictions. Given the variety of industrial fans used in the mining industry and the different operational data collected, this is yet not a trivial task in a general case. The main aim of this article is to provide reliable algorithms solving fans operational mode identification issue, which will be possible to apply in a wide range of potential applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rahmah, N., Sitanggang, I.S.: Determination of optimal epsilon (Eps) value on DBSCAN algorithm to clustering data on Peatland hotspots in sumatra. In: IOP Conference Series: Earth and Environmental Science, vol. 31, no. 1. IOP Publishing (2016)

    Google Scholar 

  2. Stefaniak, P., Kruczek, P., Śliwiński, P., Gomolla, N., Wyłomańska, A., Zimroz, R.: Bulk material volume evaluation and tracking in belt conveyor network based on data from SCADA. In: Widzyk-Capehart, E., Hekmat, A., Singhal, R. (eds.) Proceedings of the 27th International Symposium on Mine Planning and Equipment Selection-MPES 2018, pp. 335–344. Springer, Cham (2019)

    Chapter  Google Scholar 

  3. Stefaniak, P., Wodecki, J., Zimroz, R.: Maintenance management of mining belt conveyor system based on data fusion and advanced analytics. In: Timofiejczuk, A., Łazarz, B., Chaari, F., Burdzik, R. (eds.) International Congress on Technical Diagnostic, pp. 465–476. Springer, Cham (2016)

    Google Scholar 

  4. Sawicki, M., Zimroz, R., Wyłomańsk, A., Obuchowski, J., Stefaniak, P., Żak, G.: An automatic procedure for multidimensional temperature signal analysis of a SCADA system with application to belt conveyor components. Proc. Earth Planet. Sci. 15, 781–790 (2015)

    Article  Google Scholar 

  5. Wodecki, J., Stefaniak, P., Polak, M., Zimroz, R.: Unsupervised anomaly detection for conveyor temperature SCADA data. In: Timofiejczuk, A., Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds.) Advances in Condition Monitoring of Machinery in Non-Stationary Operations, vol. 9, pp. 361–369. Springer, Cham (2018)

    Chapter  Google Scholar 

  6. Benabdellah, A.C., Benghabrit, A., Bouhaddou, I.: A survey of clustering algorithms for an industrial context. Proc. Comput. Sci. 148, 291–302 (2019)

    Article  Google Scholar 

  7. Soni, N.: Aged (automatic generation of eps for dbscan). Int. J. Comput. Sci. Inf. Secur. 14(5), 536 (2016)

    Google Scholar 

  8. Yu, X., Zhou, D., Zhou, Y.: A new clustering algorithm based on distance and density. In: Proceedings of ICSSSM’05. 2005 International Conference on Services Systems and Services Management, 2005, vol. 2. IEEE (2005)

    Google Scholar 

  9. Birant, D., Kut, A.: ST-DBSCAN: An algorithm for clustering spatial–temporal data. Data Knowl. Eng. 60(1), 208–221 (2007)

    Article  Google Scholar 

  10. Viswanath, P., Pinkesh, R.: l-DBSCAN: a fast hybrid density based clustering method. In: 18th International Conference on Pattern Recognition (ICPR’06), vol. 1. IEEE (2006)

    Google Scholar 

  11. Xiaoyun, C., et al.: GMDBSCAN: multi-density DBSCAN cluster based on grid. In: 2008 IEEE International Conference on e-Business Engineering. IEEE (2008)

    Google Scholar 

  12. Ram, A., et al.: An enhanced density based spatial clustering of applications with noise. In: 2009 IEEE International Advance Computing Conference. IEEE (2009)

    Google Scholar 

  13. Borah, B., Bhattacharyya, D.K.: An improved sampling-based DBSCAN for large spatial databases. In: Proceedings of the International Conference on Intelligent Sensing and Information Processing, 2004. IEEE (2004)

    Google Scholar 

  14. Uncu, O., et al.: GRIDBSCAN: grid density-based spatial clustering of applications with noise. In: 2006 IEEE International Conference on Systems, Man and Cybernetics, vol. 4. IEEE (2006)

    Google Scholar 

  15. Liu, P., Zhou, D., Wu, N.: VDBSCAN: varied density based spatial clustering of applications with noise. In: 2007 International Conference on Service Systems and Service Management. IEEE (2007)

    Google Scholar 

  16. Khan, K., et al.: DBSCAN: past, present and future. In: The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014). IEEE (2014)

    Google Scholar 

  17. Wang, T., et al.: NS-DBSCAN: a density-based clustering algorithm in network space. ISPRS Int. J. Geo-Inf. 8(5), 218 (2019)

    Article  Google Scholar 

  18. Zhang, M.: Use density-based spatial clustering of applications with noise (DBSCAN) algorithm to identify galaxy cluster members. In: IOP Conference Series: Earth and Environmental Science, vol. 252, no. 4. IOP Publishing (2019)

    Google Scholar 

  19. Jing, W., Zhao, C., Jiang, C.: An improvement method of DBSCAN algorithm on cloud computing. Proc. Comput. Sci. 147, 596–604 (2019)

    Article  Google Scholar 

  20. Ester, M., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, no. 34 (1996)

    Google Scholar 

  21. Efremenko, V., Belyaevsky, R., Skrebneva, E.: The increase of power efficiency of underground coal mining by the forecasting of electric power consumption. In: E3S Web of Conferences. EDP Sciences, vol. 21 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. KGHM Cuprum Research and Development Centre, gen. W. Sikorskiego 2-8, 53-659, Wrocław, Poland

    Bartosz Jachnik, Paweł Stefaniak & Natalia Duda

  2. KGHM Polska Miedź S.A., M. Skłodowskiej-Curie 48, 59-301, Lubin, Poland

    Paweł Śliwiński

Authors
  1. Bartosz Jachnik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paweł Stefaniak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Natalia Duda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paweł Śliwiński
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bartosz Jachnik .

Editor information

Editors and Affiliations

  1. Wroclaw University of Economics and Business, Wrocław, Poland

    Marcin Hernes

  2. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  3. University of Newcastle, Newcastle, Australia

    Edward Szczerbicki

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jachnik, B., Stefaniak, P., Duda, N., Śliwiński, P. (2020). Clustering Algorithms in Mining Fans Operating Mode Identification Problem. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-63119-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63118-5

  • Online ISBN: 978-3-030-63119-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation