Application ANN Tool for Validation of LHD Machine Performance Characteristics


Survival of industries has become more critical in the present global competitive business environment unless they produce their projected production levels. The accomplishment of this can be possible only by maintaining the men and machinery in an efficient and effective manner. Hence, it is more essential to estimate the performance of utilized equipment for reaching/achieving future goals. The present study focuses on the estimation of underground mining machinery such as the load–haul–dump machine performance characteristics using ‘Isograph Reliability Workbench 13.0’ software. The allocation of best-fit/goodness-of-fit distribution was made by utilizing the Kolmogorov–Smirnov test (K–S) test. The parameters were recorded based on the best-fitted results using the maximum likelihood estimate test. Further, a feed-forward-back-propagation artificial neural network (ANN) tool has been used to develop the models of reliability, availability and preventive maintenance time intervals. The number of neurons was selected with the Levenberg–Marquardt learning algorithm in the hidden layer as the optimal value. The output responses were predicted corresponding to the optimal values. Further, an attempt has been made to validate the computed results with ANN predicted responses. The recommendations are suggested to the industry based on the results for the improvement of system performance.

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

Fig. 1
Fig. 2

(Source: [10])

Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11


  1. 1.

    I. Paprocka. The model of maintenance planning and production scheduling for maximizing robustness. Int. J. Prod. Res. 57(14), 1–22 (2018)

    Google Scholar 

  2. 2.

    I. Zambon, P. Andrea, P. Matyjas-łysakowska, S. Luca, M. Danilo, C. Andrea, Applied research for a safer future: exploring recent job accidents in agriculture, Italy (2012–2017). Processes 6, 1–13 (2018)

    Article  Google Scholar 

  3. 3.

    C.R. Vishnu, V. Regikumar, Reliability-based maintenance strategy selection in process plants: a case study. Procedia Technol 25, 1080–1087 (2016)

    Article  Google Scholar 

  4. 4.

    L. Swanson, An empirical study of the relationship between production technology and maintenance management. Int. J. Prod. Econ. 53, 191–207 (1997)

    Article  Google Scholar 

  5. 5.

    A.U. Adoghe, C.O.A. Awosope, S.A. Daramola, Critical review of reliability centred maintenance (RCM) for asset management in electric power distribution system. Int. J. Eng. Technol. 2, 1020–1026 (2012)

    Google Scholar 

  6. 6.

    T.O. Oyebisi, On reliability and maintenance management of electronic equipment in the tropics. Technovision 20(9), 517–522 (2000)

    Article  Google Scholar 

  7. 7.

    A.K.S. Jardine, Maintenance Replacement and Reliability (Preney Print and Litho Inc, Ontario, 1998)

    Google Scholar 

  8. 8.

    S.M. Ross, Applied Probability Models with Optimization Applications (Holden-Day, San Francisco, 2013)

    Google Scholar 

  9. 9.

    P.D.T. O’Connor, Practical Reliability Engineering, 3rd edn. (Wiley, England, 1991)

    Google Scholar 

  10. 10.

    Harish K. Ghritlahre, Development of feed-forward back-propagation neural model to predict the energy and exergy analysis of solar air heater. Trends Renew Energy 4(2), 213–235 (2018).

    Article  Google Scholar 

  11. 11.

    N.S. Harish Kumar, R.P. Choudhary, C.S.N. Murthy, Reliability-based preventive maintainability analysis of the shovel-dumper system in a surface coal mine using ANN and isograph reliability workbench. J Math Modell Eng Probl 5(4), 373–378 (2018)

    Article  Google Scholar 

  12. 12.

    S. Shakhar, Y. Haung (2001) Discovering Spatial Collocation Patterns A Summary of Rules. in Proceedings of 7th International Symposium on Spatial and Temporal Database (LA, CA, USA), pp. 236–256

  13. 13.

    I. Kapageridis. Application of Artificial Neural Network Systems in Grade Estimation from Exploration Data. Ph.D. Dissertation. Department of Mineral Resources Engineering, vol. 10 (University of Nottingham, Nottingham, UK, 1999), pp. 1–267

  14. 14.

    I. Kapageridis. Artificial neural network technology in mining and environmental applications. in Proceedings of the 11th International Symposium on Mine Planning and Equipment Selection (MPES 2002). VŠB-Technical University of Ostrava, Prague, 2002

  15. 15.

    B.R. Yama, G.T. Lineberry, Artificial neural network application for a predictive task in mining. Mining Eng. 51(2), 59–64 (1999)

    Google Scholar 

  16. 16.

    S.P. Signer, R.L. King. Evaluation of Coal Mine Roof Supports Using Artificial Intelligence. in Proceedings of the 23rd International Symposium on the Application of Computers and Operations Research in the Minerals Industries (APCOM), Arizona, USA, 1992

  17. 17.

    H. Hartman, J. Mutmansky, Introductory Mining Engineering (Wiley, Hobo-ken, 2002)

    Google Scholar 

  18. 18.

    L.A. Zadeh, Soft computing and fuzzy logic. Softw. IEEE 11, 48–56 (1994)

    Article  Google Scholar 

  19. 19.

    W.S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)

    MathSciNet  Article  Google Scholar 

  20. 20.

    J. Holland, Adaptation in Natural and Artificial Systems (University of Michigan Press, Michigan, 1975)

    Google Scholar 

  21. 21.

    L.A. Zadeh, Fuzzy logic, neural networks, and soft computing. Commun. ACM 37(3), 77–84 (1994).

    Article  Google Scholar 

  22. 22.

    J. Jang, ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993)

    Article  Google Scholar 

  23. 23.

    T. Singh, Artificial neural network approach for prediction and control of ground vibrations in mines. Min. Technol. 113, 251–256 (2004)

    Article  Google Scholar 

  24. 24.

    M. Marzouk, O. Moselhi, Selecting Earthmoving Equipment Fleets Using Genetic Algorithms. in Proceedings of the Winter Simulation Conference, (IEEE, 2002), pp. 1789–1796

  25. 25.

    C.Ö. Karacan, G.V.R. Goodman. Artificial Neural Networks to Determine Ventilation Emissions and Optimum Degasification Strategies for Longwall Mines. in Proceedings of 12th US/North American Mine Ventilation Symposium, 2008

  26. 26.

    H. Al-Chalabi, F. Ahmadzadeh, J. Lundberg, B. Ghodrati, Economic lifetime prediction of a mining drilling machine using an artificial neural network. Int. J. Min. Reclam. Environ. 28(5), 311–322 (2014)

    Article  Google Scholar 

  27. 27.

    H. Jang, E. Topal, A review of soft computing technology applications in several mining problems. Appl. Soft Comput. 22, 638–651 (2014)

    Article  Google Scholar 

  28. 28.

    J. Balaraju, M.G. Raj, C.S. Murthy, Fuzzy-FMEA risk evaluation approach for LHD machine—a case study. J. Sustain. Mining 18, 257–268 (2019)

    Article  Google Scholar 

  29. 29.

    K. Kapageridis. Artificial neural network technology in mining and environmental applications. Mine Plan. Equip. Sel. (2002)

  30. 30.

    N.S. Harish Kumar, R.P. Choudhary, C.S.N. Murthy. Failure Rate and Reliability of the Komatsu Hydraulic Excavator in a Surface Limestone Mine. in AIP Conference Proceedings, 2018, pp. 1–9

  31. 31.

    H.K. Ghritlahre, R.K. Prasad, Prediction of thermal performance of unidirectional flow porous bed solar air heater with optimal training function using artificial neural network. Energy Procedia 109, 369–376 (2017)

    Article  Google Scholar 

  32. 32.

    I.M. Chakravarti, R.G. Laha, J. Roy, Handbook of Methods of Applied Statistics, vol. I (Wiley, Hoboken, 1967), pp. 392–394

    Google Scholar 

  33. 33.

    J. Balaraju, G.M. Raj, C.S.N. Murthy, Reliability analysis and failure rate evaluation of load haul dump machines using Weibull distribution analysis. J. Math. Model. Eng. Probl. 5(2), 116–122 (2018)

    Google Scholar 

Download references


The authors are thankful to the general manager of HRD, Vedanta Group of Mines, Udaipur, Rajasthan, India, for allowing us to conduct the field visit for the collection of required data for the present analysis.

Author information



Corresponding author

Correspondence to Balaraju Jakkula.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author (Mr. Balaraju Jakkula, Research Scholar, Department of Mining Engineering, NITK Surathkal, India) states that there is no conflict of interest to publish our original research work. The data sets used in this analysis were completely authorized, and there is no financial or personal relationship with a third party. There is no conflict of interest among all the authors. We are showing our interest to publish our manuscript in your prestigious journal.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Balaraju Jakkula: Research Scholar, Govinda Raj Mandela: Professor, Suryanaraya Murthy Chivukula: Professor.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jakkula, B., Mandela, G.R. & Chivukula, S.M. Application ANN Tool for Validation of LHD Machine Performance Characteristics. J. Inst. Eng. India Ser. D 101, 27–38 (2020).

Download citation


  • Performance
  • Reliability
  • Availability
  • Maintenance
  • LHD
  • ANN