Enhanced K-Nearest Neighbors Method Application in Case of Draglines Reliability Analysis

Dragline’s availability plays a major role in sustaining economicfeasibility and operation of opencast coal mine. Thus, its reliability is essentialfor the production availability of mine. The dragline’s reliability and maintenanceoptimization are key issues, which should seriously be considered. Draglines’unexpected failures and consequently unavailability result in delayed productionsand increased maintenance and operating costs. The applications ofmethodologies which can predict the failure mode of dragline based on thehistorical dataset of failure are not only useful to reduce the maintenance andoperating costs but also increase the availability and the production rate of miningmachineries. In this research a historical failure dataset of a dragline has beenutilized in order to analyze and conduct predictive maintenance. Authors havealready utilized the K-Nearest Neighbors (KNN) algorithm in order to predict thefailure mode; however, there was a chance of getting into local optimum byutilization of the mentioned methodology. In this case, combination of GeneticAlgorithm and K-Nearest Neighbor algorithm (i.e. called enhanced K-NearestNeighbors) was applied for the failure dataset, so the probability of localoptimum has been decreased by application of Genetic Algorithm. In previousstudies, the Artificial Neural Network methods and conventional method of KNearestNeighbor has been applied to the same dataset, yet the result fromenhanced K-Nearest Neighbor reveals better regression analysis.ISBN för värdpublikation: 978-3-319-99219-8, 978-3-319-99220-4</p