Faculty Publications
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Item Reliability Analysis of LHD Machine - A Case Study(Springer Nature, 2020) BalaRaju, J.; Govinda Raj, M.; Murthy, C.S.N.In the present global scenario, survival of the industry is more critical unless it produces their intended targets. Accomplishment of expected rate of production levels are depends on the performance of equipment. Hence, it is very important to predict the maintenance schedules for replacement or repair actions of the defective parts. Keeping in view, every industry is constantly looking for enhancement equipment life. Reliability analysis is one of the well appropriated techniques used to estimate the life of the equipment. In this paper, performance of Load-Haul-Dumper (LHD) has been analyzed. Renewal process approach has been utilized for reliability investigation. Best fit distribution of data sets were made by the utilization of Kolmogorov-Smirnov (K-S) test. Parametric estimation of theoretical probability distributions was done by utilizing Maximum Likelihood Estimate (MLE) method. Reliability of each individual sub-system has been computed according to the best fit distribution. In addition to that, reliability based preventive maintenance (PM) time schedules were calculated for the expected 90% reliability level. The possible recommendations were suggested for improvement of reliability level. © 2020, Springer Nature Switzerland AG.Item Reliability analysis and failure rate evaluation of load haul dump machines using Weibull distribution analysis(International Information and Engineering Technology Association info@iieta.org, 2018) Balaraju, R.J.; Govinda, R.M.; Murthy, C.S.N.Improvement of multifaceted system quality requires a group of complex design modifications. An expanding complexity of system is potentially prone to increase in the failure frequency. Continuous and random occurrence of failures in a system could be the main cause for performance drop of machinery. Theoritical probability distribution is one of the techniques used to estimate the lifetime of a system and its sub-systems with several failure considerations. One of the most extensively used statistical approaches for reliability estimation is a Weibull distribution. In the present paper a three-parameter Weibull distribution approach was adopted to analyze the data sets of Load-Haul-Dumper (LHD) in underground mines using 'Isograph Reliability Workbench 13.0' software package. The parameters were evaluated using best fit distributions and Weibull likelihood plots. Percentage reliability of each individual subsystem of LHD was estimated. Further, an attempt has been made to identify the preventive maintenance (PM) time intervals for enhancing the expected rate of reliability. © 2017 IIETA.Item Performance Evaluation of Underground Mining Machinery: A Case Study(Springer, 2020) BalaRaju, J.; Govinda Raj, M.; Murthy, C.S.N.Unexpected occurrence of uneven breakdowns and their consequences have a significant influence on the equipment life. Hence, there is a need to discover the motives for the happening of critical potential failures and required repair or replacement action to control. Reliability analysis is utilized to approximate the performance of the equipment. In this study, the performance of the underground mining machinery known as load haul dumper (LHD) has been estimated with reliability analysis. The best-fit distribution of the data sets was selected by testing the numerous statistical distributions using the Kolmogorov–Smirnov (K-S) test. The percentage of reliability of each subsystem of the LHD machine was computed based on the best-fit approximation. The overall system reliability of the equipment was estimated using a series configuration-based reliability block diagram (RBD) approach. The reliability-based preventive maintenance (PM) time intervals were also computed for estimated 90.00% reliability. To accomplish the desired level of reliability, a review on maintenance programs should be made. Possible recommendations were made to the maintenance department in the industry for improvement in equipment. © 2020, ASM International.