Development of Expert System Shell for Coal Mining Industry

  • Piotr Przystałka
  • Wojciech Moczulski
  • Anna Timofiejczuk
  • Mateusz Kalisch
  • Marek Sikora
Conference paper
Part of the Applied Condition Monitoring book series (ACM, volume 4)


The paper deals with the design of an expert system shell for the decision support system that is developed to be used in coal mining industry. A proposed architecture of the system allows reasoning by means of multi-domain knowledge representations and multi-inference engines. The implementation of the system is based on data mining software (RapidMiner) which makes possible to acquire domain-specific knowledge and its application in the expert system shell. In this study, the preliminary verification is presented using DAMADICS simulator that was proposed to compare different fault diagnosis methods. The obtained results show the merits and limitations of the proposed approach.


Diagnostic expert systems Knowledge representations Machine learning Data mining Fault detection and isolation 



The research presented in the paper was partially financed by the National Centre of Research and Development (Poland) within the frame of the project titled “Zintegrowany, szkieletowy system wspomagania decyzji dla systemów monitorowania procesów, urzadzeń i zagrożeń” (in Polish) carried out in the path B of Applied Research Programme—grant No. PBS2/B9/20/2013. The part of the research was also financed from the statutory funds of the Institute of Fundamentals of Machinery Design.


  1. 1.
    Cholewa W (2004) Expert systems in technical diagnostics. In: Korbicz J, Kowalczuk Z, Kościelny J, Cholewa W (eds) Fault diagnosis. Springer, Berlin, pp 591–631CrossRefGoogle Scholar
  2. 2.
    Liebowitz J (1997) The handbook of applied expert systems. CRC Press LLC, Boca RatonGoogle Scholar
  3. 3.
    Yang J, Ye C, Zhang X (2001) An expert system shell for fault diagnosis. Robotica 19:669–674Google Scholar
  4. 4.
    Grayson RL, Watts CM, Singh H, Yuan S, Dean JM, Reddy NP, Nutter RS Jr (1990) A knowledge-based expert system for managing underground coal mines in the us. IEEE Trans Ind Appl 26(4):98–604CrossRefGoogle Scholar
  5. 5.
    Sahu HB, Pal BK (1996) Knowledge based expert system to assess fires and pollution to fires in underground coal. J Min Met Fuels 44(6–7)Google Scholar
  6. 6.
    Zhang H, Zhao G (1999) CMEOC an expert system in the coal mining industry. Expert Syst Appl 16:73–77CrossRefGoogle Scholar
  7. 7.
    Yingxu Q, Hongguo Y (2010) Design and application of expert system for coal mine safety. In: Second IITA international conference on geoscience and remote sensingGoogle Scholar
  8. 8.
    Wang C, Wang Z (2010) Design and implementation of safety expert information management system of coal mine based on fault tree. J Softw 5(10):1114–1120Google Scholar
  9. 9.
    Golak S, Wieczorek T (2014) Koncepcja system ekspertowego do oceny i poprawy ekoefektywności kopalń. Studia Informatica 116(2):213–222Google Scholar
  10. 10.
    Chekushina EV, Vorobev AE, Chekushina TV (2013) Use of expert systems in the mining. Middle-East J Sci Res 18:1–3. doi: 10.5829/idosi.mejsr.2013.18.1.12345 Google Scholar
  11. 11.
    THOR main control system website, February 2015. Available at,25,THOR.html
  12. 12.
    SMoK system website—monitoring system for machinery and equipment, February 2015. Available at,18,2,48
  13. 13.
    Weka project website, October 2014. Available at
  14. 14.
    R project website, October 2014. Available at
  15. 15.
    Orange software website, October 2014. Available at
  16. 16.
    Statistica software website, October 2014. Available at
  17. 17.
    Angoss knowledge studio website, October 2014. Available at
  18. 18.
    Akthar F, Hahne C (2012) Rapid Miner 5 Operator Reference. Rapid-I GmbH. Available at
  19. 19.
    Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, New JerseyCrossRefGoogle Scholar
  20. 20.
    Woźniak M, Grana M, Corchado E (2014) A survey of multiple classifier systems as hybrid systems. Inf Fusion 16:3–17 (Special Issue on Information Fusion in Hybrid Intelligent Fusion Systems)Google Scholar
  21. 21.
    Bartyś M, Patton R, Syfert M, de las Heras S, Quevedo J (2006) Introduction to the damadics actuator fdi benchmark study. Control Eng Pract 14(6):577–596CrossRefGoogle Scholar
  22. 22.
    Korbicz J, Kowal M (2007) Neuro-fuzzy networks and their application to fault detection of dynamical systems. Eng Appl Artif Intell 20(5):609–617,doi: 10.1016/j.engappai.2006.11.009 CrossRefGoogle Scholar
  23. 23.
    Mrugalski M, Witczak M, Korbicz J (2008) Confidence estimation of the multi-layer perceptron and its application in fault detection systems. Eng Appl Artif Intell 21:895–906CrossRefGoogle Scholar
  24. 24.
    Puig V, Witczak M, Nejjari F, Quevedo J, Korbicz J (2007) A GMDH neural network-based approach to passive robust fault detection using a constraint satisfaction backward test. Eng Appl Artif Intell 20:886–897CrossRefGoogle Scholar
  25. 25.
    Kabiesz J, Sikora B, Sikora M, Wróbel Ł (2013) Application of rule-based models for seismic hazard prediction in coal mines. Acta Montanist Slovaca 18(4):262–277Google Scholar
  26. 26.
    Riza LS, Janusz A, Bergmeir C, Cornelis C, Herrera F, S’lezak D, Benítez JM (2014) Implementing algorithms of rough set theory and fuzzy rough set theory in the r package ȑoughsets. Inf Sci 287:68–89CrossRefGoogle Scholar
  27. 27.
    Krasuski J, Jankowski A, Skowron A, Ślezak D (2013) From sensory data to decision making: a perspective on supporting a fire commander. In: Web intelligence/IAT workshops, pp 229–236Google Scholar
  28. 28.
    Wyczółkowski R (2008) Intelligent monitoring of local water supply system pp 3–36Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Piotr Przystałka
    • 1
  • Wojciech Moczulski
    • 1
  • Anna Timofiejczuk
    • 1
  • Mateusz Kalisch
    • 1
  • Marek Sikora
    • 2
    • 3
  1. 1.Institute of Fundamentals of Machinery DesignSilesian University of TechnologyGliwicePoland
  2. 2.Institute of Innovative Technologies EMAGKatowicePoland
  3. 3.Institute of Computer SciencesSilesian University of TechnologyGliwicePoland

Personalised recommendations