Application of Genetic Algorithms for Reliability Assessment of Two Mine Hoisting Systems

摘要

This paper discusses the application of a computerised model based on genetic algorithms (GAs),rncalled GenRel, for reliability assessment of underground mine hoisting systems. The purpose ofrnthis paper is to investigate whether GenRel can be applied to predict future failure data of a minernhoisting system based on historical records of failures. The incentive of selecting the GAs forrnprediction of failures is that GAs are a class of evolutionary algorithms which imitate the biologicalrnevolution procedures such as reproduction, selection, crossover and mutation. The reliability ofrnmining equipment changes over time due to an array of factors (eg equipment age, the operatingrnenvironment, number and quality of repair). These factors affect the equipment’s failure patternsrnand have complex impacts on the equipment’s reliability characteristics. The failure patterns arernassumed to follow a biological evolution process, and thus GAs can be considered applicable in thernmodelling process.rnTo conduct the reliability assessment, a study was carried out with historical failure data of hoists inrnthe time period from January to December 2007. Two failure data sets of two mine hoisting systemsrnwere collected from two typical underground mines in Ontario, Canada, which are denoted as thernNA mine and the SB mine. The failure data sets were prepared in the format of time between failuresrn(TBF). Then, these data sets were entered in GenRel to generate predicted failure data sets for thernperiod of January to March 2008. The paper discusses the statistical similarity of the actual failurerndata sets for the period of January to March 2008 with the predicted failure data set generated byrnGenRel in the same time period.