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

Abstract

Dragline's availability plays a major role in sustaining economic feasibility and operation of opencast coal mine. Thus, its reliability is essential for the production availability of mine. The dragline's reliability and maintenance optimization are key issues, which should seriously be considered. Draglines' unexpected failures and consequently unavailability result in delayed productions and increased maintenance and operating costs. The applications of methodologies which can predict the failure mode of dragline based on the historical dataset of failure are not only useful to reduce the maintenance and operating costs but also increase the availability and the production rate of mining machineries. In this research a historical failure dataset of a dragline has been utilized in order to analyze and conduct predictive maintenance. Authors have already utilized the K-Nearest Neighbors (KNN) algorithm in order to predict the failure mode; however, there was a chance of getting into local optimum by utilization of the mentioned methodology. In this case, combination of Genetic Algorithm and K-Nearest Neighbor algorithm (i.e. called enhanced K-Nearest Neighbors) was applied for the failure dataset, so the probability of local optimum has been decreased by application of Genetic Algorithm. In previous studies, the Artificial Neural Network methods and conventional method of K-Nearest Neighbor has been applied to the same dataset, yet the result from enhanced K-Nearest Neighbor reveals better regression analysis.

Physics‐based and data‐driven approaches for lifetime estimation under variable conditions: Application to organic light‐emitting diodes

“…21 As an emerging trend in the prognostics approach, KNN has been also used as a failure type prediction tool. 23…”

“…KNN belongs to similarity‐based prognostics and has been employed as a lifetime estimation tool in the prognostics and health management studies for LEDs, 21 electromagnetic relays contact resistance, 22 and printed circuit boards 21 . As an emerging trend in the prognostics approach, KNN has been also used as a failure type prediction tool 23 …”

Industrial Equipment’s Throughput Capacity Analysis

“…Also, open-pit mine equipment's such as wagon drill [12], shovel [13], and truck [14], [15], have been analyzed in other studies using a statistical approach. Artificial intelligence techniques such as Genetic Algorithm (GA) [16], [17] and Machine Learning (ML) [18], [19] have been used for reliability and maintainability analysis and maintenance management of the mining equipment. In this paper, the statistical method is preferred due to the lack of data and userfriendly statistical procedures for managers and practitioners.…”