Owing to increases in the costs of extracting ores from underground and labor costs, mining companies are urged to deploy equipment with a high degree of mechanization and automation. While increased mechanization and automation make considerable contributions to mine productivity, unexpected equipment failures and planned or routine maintenance prohibit the maximum possible utilization of sophisticated mining equipment and require significant amount of extra capital investment. Therefore from an operations management perspective, it is desired that mining equipment is reliable enough to meet production targets and at the same time is cost-effective to maintain. This thesis deals with aspects of reliability prediction for mining machinery. A useful software tool called GenRel was developed for this purpose at Laurentian University Mining Automation Laboratory (LUMAL). In GenRel it is assumed that failures of mining equipment caused by an array of factors follow the biological evolution theory. GenRel then simulates the failure occurrences during a time period of interest using genetic algorithms (GAs) coupled with a number of statistical techniques.

In this thesis the author endeavors to improve GenRel. First, a first-ever discrete probability distribution function is merged with continuous probability distribution functions pool already existing in GenRel. Second, GenRel is made capable of replicating a prediction run when required by users to add more flexibility in reliability and maintainability prediction. Third, two genetic operators in the genetic algorithm substitute preexisting ones to accelerate the convergence process when analysis of large amount of data is desired and make user parameter setting more intuitive.

Upon successful completion of the above improvements of GenRel, the author runs three groups of case studies. The objectives of case studies include an assessment of the applicability of GenRel using real-life data and an investigation of the impacts of data size and chronological sequence on predictions. The data used in these case studies is compiled from failure records of two hoist systems at different mine sites and from one load-haul-dump (LHD) vehicle out of an LHD fleet from the Sudbury area in Ontario, Canada.

The first group involves reliability analysis and predictions for both a 3-month period and a 6-month period of time of a hoist system. The second group of case studies investigates the applicability of GenRel as a reliability analysis tool using historical failure data from another mine hoist system with two different time intervals, three months and six months. The third group of case studies focuses on maintainability analysis and prediction of an LHD vehicle with two different time intervals, three months and six months. In each prediction case study, a statistical test is carried out to examine the similarity between the predicted data set with the real-life data set in the same time period.

In the discussion and conclusion part, the author discloses the findings from the case studies and suggests future research direction.