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.
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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.
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.
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Balaraju Jakkula: Research Scholar, Govinda Raj Mandela: Professor, Suryanaraya Murthy Chivukula: Professor.
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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). https://doi.org/10.1007/s40033-019-00203-3