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While increased mechanization and automation make considerable contributions to mine productivity, unexpected equipment failures and imperfect planned or routine maintenance prohibit the maximum possible utilization of sophisticated mining equipment and require significant amount of extra capital investment. Traditional preventive/planned maintenance is usually scheduled at a fixed interval based on maintenance personnel's experience and it can result in decreasing reliability. This paper deals with reliability analysis and prediction for mining machinery. A software tool called GenRel is discussed with its theoretical background, applied algorithms and its current improvements. In GenRel, it is assumed that failures of mining equipment caused by an array of factors (e.g. age of equipment, operating environment) follow the biological evolution theory. GenRel then simulates the failure occurrences during a time period of interest based on Genetic Algorithms (GAs) combined with a number of statistical procedures. The paper also discusses a case study of two mine hoists. The purpose of this paper is to investigate whether or not GenRel can be applied for reliability analysis of mine hoists in real life.

Statistical testing methods are applied to examine the similarity between the predicted data set with the real-life data set in the same time period. The data employed in this case study is compiled from two mine hoists from the Sudbury area in Ontario, Canada. Potential applications of the reliability assessment results yielded from GenRel include reliability-centered maintenance planning and production simulation.

The case studies shown in this paper demonstrate successful applications of a GAs-based software, GenRel, to analyze and predict dynamic reliability characteristics of two hoist systems. Two separate case studies in Mine A and Mine B at a time interval of three months both present acceptable prediction results at a given level of confidence, 5 percent.

Potential applications of the reliability assessment results yielded from GenRel include reliability-centered maintenance planning and production simulation.

Compared to conventional mathematical models, GAs offer several key advantages. To the best of the authors’ knowledge, there has not been a wide application of GAs in hoist reliability assessment and prediction. In addition, the authors bring discrete distribution functions to the software tool (GenRel) for the first time and significantly improve computing efficiency. The results of the case studies demonstrate successful application of GenRel in assessing and predicting hoist reliability, and this may lead to better preventative maintenance management in the industry.

The purpose of this paper is to formulate, develop and test a reliability assessment model (GenRel) based on genetic algorithms.

Using genetic algorithm based modelling technique, a computer model was developed to predict mine equipment failures from historical data. Two different approaches in application of this technique are demonstrated.

A case study representing a test for convergence of the model was successfully performed. This is an indicator that GenRel can be used to predict equipment failures using a genetic algorithm based modeling technique.

The use of classical statistical techniques has proven to be an effective tool for reliability analysis of mining equipment. This paper presents an efficient alternative to these classical probability based reliability analysis methods. GenRel is a software solution which performs predictive reliability based upon genetic algorithms (GAs). The advantage of using this technique is the fact that the assumptions based on GAs are much simpler compared to classical statistical methods. The computer model is developed to accept a variety of user input data, most importantly, the ability to use real life historical data in the form of Time Between Failures (TBFs) or Time To Repair (TTRs).

The proposed research offers an alternative method to conventional statistically based reliability analysis and may lead to the foundation of a new approach for reliability assessment with potential applications in other industrial fields as well.

This chapter explores the complexity of issues surrounding Black males and athletics in higher education. Multiple studies over the past decade and a half have depicted an oppositional relationship between athletics and academic achievement. Research suggests that media imagery, stereotyping, and other non-academic influences on African American males who participate in intercollegiate athletics tend to result in an over-identification with professional athletes, sports, and perceptions of great value associated with physical performance activities and a simultaneous under-identification with academic performance, scholarly identity, and student development. These pressures ultimately limit career options outside of athletics. In an effort to combat these issues, Beyond the Game™ (BTG) Program, a program described in this chapter that was developed in Wisconsin’s Equity and Inclusion Laboratory (Wei LAB) and implemented at the University of Wisconsin-Madison, seeks to harness curricular, co-curricular, and on-the-field leadership training to strategically develop and support post-graduation options. This comprehensive, multi-faceted program directly confronts the challenges student-athletes face when they exhaust their eligibility status but have yet to identify viable career alternatives to professional sports. This chapter explores the main tenants of the program, established with a group of Division 1 NCAA-affiliated college athletes as participants.