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Equipment failures and associated maintenance have an impact on the profitability of mines. Implementing maintenance at suitable time intervals can save money and improve the reliability and maintainability of mining equipment. This paper discusses aspects of maintainability prediction for mining machinery. For this purpose, a software tool, called GenRel, is developed. In GenRel, it is assumed that failures of mining equipment caused by an array of factors follow the biological evolution process. GenRel then simulates the failure occurrences during a time period of interest using Genetic Algorithms (GAs) coupled with a statistical methodology. Two case studies on maintainability analysis and prediction of a mine’s hoist system in two different time intervals, three months and six months are discussed. The data are collected from a typical underground mine in the Sudbury area in Ontario, Canada. In each case study, a statistical test is carried out to examine the similarity between the predicted data set and the real-life data set in the same time period. The objectives include an assessment of the applicability of GenRel using real-life data and an investigation of the relationship between data size and prediction results. Discrete and continuous probability distribution functions are applied to the input data.

Maintainability Analysis Software of Mine’s Hoist System Based on Genetic Algorithms for Data Collection Periods of Three and Six Months