Abstract

Reliability is an important consideration in the planning, design and operation of engineering systems. As the size and complexity of mining equipment continue to increase, the implications of equipment failure become ever more critical. An unplanned failure can result in significantly higher repair costs than a planned maintenance or repair. Of even more importance is the loss of production associated with larger equipment failures. One method to mitigate the impact of failures is to improve the reliability of the equipment. Reliability is a performance indicator of overall equipment condition. A first step in reliability improvement is collection and analysis of the appropriate data. This paper presents a case study describing reliability analysis of crushing plants in Jajarm bauxite mine. In this study crushing plants are divided into seven subsystems. Reliability analysis has been done for each subsystem by using failures data. The parameters of some idealized probability distributions, such as Weibull, Exponential, Lognormal distributions, have been estimated by using ReliaSoft's Weibull-H- 6 software. An investigation has also been made to determine which of these distributions provide the best fit for characterizing the failure pattern of the two crushing plants and their subsystems. Some aspects of system failure behavior are analyzed briefly for ongoing machine improvement. Reliability of both crushing plants and its subsystems has been estimated at different mission times with their best fit distribution. Analysis of the total downtime, breakdown frequency, reliability, and maintainability characteristics of different subsystems shows that the reliability of crushing plant 1 and crushing plant 2 after 10 hour reduce to about 64% and 35% respectively. The study shows that reliability and maintainability analysis is very useful for deciding maintenance intervals. It is also useful for planning and organizing maintenance.

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