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 Effective Maintenance Planning for Improving the Reliability of Underground Mining Equipment—A Case Study(Acadlore Publishing Services Limited, 2025) Balaraju, B.; Raj, M.G.; Tripathi, A.K.The Load Haul Dumper (LHD) is essential machinery utilized for moving ore in the underground mining industry, in order to fulfil production targets. In this connection, the efficiency of the equipment should be maintained at an ideal standard, to be accomplished by reducing unexpected failure of components or subsystems in this intricate system. Downtime analysis helped identify faulty components and subsystems, which require the development of complementary maintenance plans to facilitate the replacement or fixing of parts. Proper practices of maintenance management improve the performance of the equipment. In this research, the efficiency of the LHD machine was assessed through reliability methods. Initially, the assumption of independent and identical distribution (IID) for the data sets was validated using trend and serial correlation analyses. The statistical tests indicated that the data sets adhered to the IID assumption. Therefore, a renewal process method was utilized for additional examination. The Kolmogorov-Smirnov (K-S) test was utilized to identify the most suitable distribution for the data sets. The theoretical probability distributions were estimated parametrically using the Maximum Likelihood Estimate (MLE) approach. The dependability of each separate subsystem was determined using the optimal fit distribution. Based on the reliability outcomes, preventive maintenance (PM) time plans were created to reach the targeted 90% reliability. Different maintenance strategies, in addition, were suggested to the maintenance team to extend the lifespan of the machine. © 2025 by the author(s). Licensee Acadlore Publishing Services Limited, Hong Kong.