Application of Machine Learning for Dragline Failure Prediction

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2017-04-26

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TAGHIZADEH, Amir
Demirel, Nuray

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Overburden stripping in open cast coal mines is extensively carried out by walking draglines. Draglines’ unavailability and unexpected failures result in delayed productions and increased maintenance and operating costs. Therefore, achieving high availability of draglines plays a crucial role for increasing economic feasibility of mining projects. Applications of methodologies which can forecast the failure type of dragline based on the available failure data not only help to reduce the maintenance and operating costs but also increase the availability and the production rate. In this study, Machine Learning approaches have been applied for data which has been gathered from an operating coal mine in Turkey. The study methodology consists of three algorithms as: i) implementation of K-Nearest Neighbors, ii) implementation of MultiLayer Perceptron, and iii) implementation of Radial Basis Function. The algorithms have been utilized for predicting the draglines’ failure types. In this sense, the input data, which are mean time-to-failure, and the output data, failure types, have been fed to the algorithms. The regression analysis of methodologies have been compared and showed the K- Nearest Neighbors has a higher rate of regression which is around 70 percent. Thus, the K-Nearest Neighbor algorithm can be applied in order to preventive components replacement which causes to minimized preventive and corrective cost parameters. The accurate prediction of failure type, indeed, causes to optimized number of inspections. The novelty of this study is application of machine learning approaches in draglines’ reliability subject for first time.

URI

https://hdl.handle.net/11511/35063

DOI

https://doi.org/10.1051/e3sconf/20171503002

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Department of Mining Engineering, Conference / Seminar

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Citation Formats

A. TAGHIZADEH and N. Demirel, “Application of Machine Learning for Dragline Failure Prediction,” 2017, vol. 15, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35063.