Faculty Publications
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Item Application ANN Tool for Validation of LHD Machine Performance Characteristics(Springer, 2020) Balaraju, B.; Raj, G.R.; Murthy, C.S.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. © 2020, The Institution of Engineers (India).Item Reliability, availability and maintainability (RAM) investigation of Load Haul Dumpers (LHDs): a case study(Springer, 2022) Balaraju, B.; Raj, G.R.; Murthy, S.M.Load Haul Dumpers (LHDs) are prominent equipment employed for transportation operations in many of the underground mines. This equipment often suffers from frequent breakdowns due to a variety of technical and managerial practices resulting in increased maintenance costs and loss of production and productivity. Reliability, Availability and Maintainability (RAM) analysis deal with the optimal functioning of equipment, maintenance scheduling, controlling cost, and improvement of availability and performance. Keeping this in view, the current study focused on the estimation of the performance of the equipment using RAM investigation. The required failure and repair data of LHDs were collected from field investigations. Graphical analyses using Trend and serial correlation tests and analytical analysis using Statistic-U test were conducted to validate the Independent and Identical Distribution (IID) nature of the data sets. Based on the above tests, the Renewal Process was adopted to carry out the RAM analysis. The best-fit approximation of datasets was selected by performing the Kolmogorov–Smirnov (K–S) test. In addition to that, the reliability-based Preventive Maintenance time intervals were estimated to improve the percentage of reliability. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.