A Review of Reliability and Fault Analysis Methods for Heavy Equipment and Their Components Used in Mining

Prerita Odeyar, Derek B. Apel (), Robert Hall, Brett Zon and Krzysztof Skrzypkowski ()
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Prerita Odeyar: School of Mining and Petroleum Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
Derek B. Apel: School of Mining and Petroleum Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
Robert Hall: Department of Mining Engineering and Management (MEM), South Dakota School of Mines, Rapid City, SD 57701, USA
Brett Zon: North American Construction Group, 27287-100 Avenue, Acheson, AB T7X 6H8, Canada
Krzysztof Skrzypkowski: Faculty of Civil Engineering and Resource Management, AGH University of Science and Technology, 30-059 Kraków, Poland

Energies, 2022, vol. 15, issue 17, 1-27

Abstract: To achieve a targeted production level in mining industries, all machine systems and their subsystems must perform efficiently and be reliable during their lifetime. Implications of equipment failure have become more critical with the increasing size and intricacy of the machinery. Appropriate maintenance planning reduces the overall maintenance cost, increases machine life, and results in optimized life cycle costs. Several techniques have been used in the past to predict reliability, and there’s always been scope for improvement of the same. Researchers are finding new methods for better analysis of faults and reliability from traditional statistical methods to applying artificial intelligence. With the advancement of Industry 4.0, the mining industry is steadily moving towards the predictive maintenance approach to correct potential faults and increase equipment reliability. This paper attempts to provide a comprehensive review of different statistical techniques that have been applied for reliability and fault prediction from both theoretical aspects and industrial applications. Further, the advantages and limitations of the algorithm are discussed, and the efficiency of new ML methods are compared to the traditional methods used.

Keywords: reliability; fault diagnosis; predictive maintenance; machine learning; lifetime distributions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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