Mining equipment reliability assessment models based on genetic algorithms and theoretical probability distributions

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This proposal is a continuation of an NSERC grant which attempts to develop and test a mining equipment reliability assessment methodology based on Genetic Algorithms (GAs) with theoretical probability distributions as the fitness functions for the GAs models. GAs are stochastic search techniques based on the principles of natural selection, heredity and genetics. The reason for selecting GAs is the fact that the reliability of mining equipment changes over time due to its dependence upon several covariates/factors (e.g. the operating environment, quality of repair). These factors combine to create a complex impact on a piece of equipment's reliability function. This impact encapsulates and inherits to some degree the individual characteristics of the factors as they evolve over time. The overall idea is to investigate whether novel reliability assessment models based on GAs can produce satisfactory solutions compared to classical reliability analysis. So far, an Exponential distributions based GAs model and a Lognormal distributions based model have been developed and tested using collected data in the form of times between failures from underground mines. These two probability distributions have been found to fit historical failure data. However, the data indicated that the criteria of convergence does not suffice and it is imperative to develop models based on other probability distr. for achieving higher predictability. This proposal will first focus on the development and testing of new models and their comparison with the existing GAs models. Our previous research, on the combination of mine production simulation studies with GAs based reliability assessment models for analyzing the impact of mobile equipment failures on the productivity of mines, resulted in the development of a prototype mine simulator. The next phase in the development of this simulator will require the implementation of higher functionality to better represent real life mine case studies.