Reliability investigation of diesel engines used in dumpers by the Bayesian approach
Abstract
Mining is a global multibillion dollar industry. The growing complexity of mining equipment and systems often leads to failures. As a consequence, reliability, maintainability and availability of mining equipment has come to the forefront (Kunar et al., 2013). Dump trucks are used for transporting ore in open pit mines. The most critical subsystem of these
trucks is the diesel engine. Failure of the engine stops the entire operation which results in loss of revenue from production. For reducing downtime, changes in maintenance policies is necessary (Sevasar, 2013). For changing maintenance strategies of the engine, assessment of reliability of its subsystems becomes vital. In this study, a reliability assessment of an engine and its subsystems is carried out. The engine is divided into different subsystems. Trend analysis of Time Between Failure (TBF) data collected for each subsystem is performed. The engine TBF data are treated into four types of probability distributions: Weibull, Exponential, Normal and Lognormal. The MLE method from Minitab software is used for estimating the parameters of distribution required to determine the reliability of the subsystems. Although the TBF data is collected for three years, the failure data of each engine subsystem contains sparse failure data. Hence, for analysis purpose, the collected
data has been grouped for three of the same types of engines. To supplement the result, 100 failure data examples have been generated by the MCS technique. To estimate the reliability for each subsystem of a single engine without grouping the TBF data, the Bayesian method is used. Using reliability analysis, failure of components of engines is predicted in order to take up maintenance at the right time with an aim to reduce the maintenance cost.
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