Using GenRel for reliability assessment of mining equipment

Abstract

PurposeThe purpose of this paper is to formulate, develop and test a reliability assessment model (GenRel) based on genetic algorithms.Design/methodology/approachUsing 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.FindingsA 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.Practical implicationsThe 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).Originality/valueThe 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.