Sequential Pattern Mining Method for Predictive Maintenance of Large Mining Trucks

Abdulgani Kahraman; Mehmed Kantardzic; M. Mustafa Kahraman; Muhammed Kotan

doi:10.1007/978-3-030-72805-2_9

Sequential Pattern Mining Method for Predictive Maintenance of Large Mining Trucks

Abdulgani Kahraman^*, Mehmed Kantardzic, M. Mustafa Kahraman, Muhammed Kotan

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

In recent decades, various solutions had been sought for reducing operating costs while increasing the production of minerals in mining operations. Equipment health monitoring technologies had been used for monitoring and increasing the availability of machines. However, the data obtained from these technologies had only been used for monitoring the equipment health, and not for the prediction of failures. In this paper, it was relied on alarms and signals collected through real-time health monitoring technologies for predicting crucial mining truck failures. Sequential Pattern Mining (SPM) Method for Predictive Maintenance had been developed and implemented as a methodology to discover which group of alarms and signals might be related to specific truck failures. The results indicate that the SPM method is able to detect machine failures of trucks with high accuracy with an average 96%. The proposed methodology may reduce the maintenance time, and the expenditures caused by truck breakdowns in the mining industry.

Original language	English
Title of host publication	Mediterranean Forum – Data Science Conference - First International Conference, MeFDATA 2020, Revised Selected Papers
Editors	Jasminka Hasic Telalovic, Mehmed Kantardzic
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	126-136
Number of pages	11
ISBN (Print)	9783030728045
DOIs	https://doi.org/10.1007/978-3-030-72805-2_9
Publication status	Published - 2021
Externally published	Yes
Event	1st Mediterranean Forum - Data Science Conference, MeFDATA 2020 - Virtual, Online Duration: 24 Oct 2020 → 24 Oct 2020

Publication series

Name	Communications in Computer and Information Science
Volume	1343 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	1st Mediterranean Forum - Data Science Conference, MeFDATA 2020
City	Virtual, Online
Period	24/10/20 → 24/10/20

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Mine equipment
Mining trucks
Predictive maintenance
Sequential pattern mining

Access to Document

10.1007/978-3-030-72805-2_9

Cite this

Kahraman, A., Kantardzic, M., Kahraman, M. M., & Kotan, M. (2021). Sequential Pattern Mining Method for Predictive Maintenance of Large Mining Trucks. In J. Hasic Telalovic, & M. Kantardzic (Eds.), Mediterranean Forum – Data Science Conference - First International Conference, MeFDATA 2020, Revised Selected Papers (pp. 126-136). (Communications in Computer and Information Science; Vol. 1343 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-72805-2_9

Kahraman, Abdulgani ; Kantardzic, Mehmed ; Kahraman, M. Mustafa et al. / Sequential Pattern Mining Method for Predictive Maintenance of Large Mining Trucks. Mediterranean Forum – Data Science Conference - First International Conference, MeFDATA 2020, Revised Selected Papers. editor / Jasminka Hasic Telalovic ; Mehmed Kantardzic. Springer Science and Business Media Deutschland GmbH, 2021. pp. 126-136 (Communications in Computer and Information Science).

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title = "Sequential Pattern Mining Method for Predictive Maintenance of Large Mining Trucks",

abstract = "In recent decades, various solutions had been sought for reducing operating costs while increasing the production of minerals in mining operations. Equipment health monitoring technologies had been used for monitoring and increasing the availability of machines. However, the data obtained from these technologies had only been used for monitoring the equipment health, and not for the prediction of failures. In this paper, it was relied on alarms and signals collected through real-time health monitoring technologies for predicting crucial mining truck failures. Sequential Pattern Mining (SPM) Method for Predictive Maintenance had been developed and implemented as a methodology to discover which group of alarms and signals might be related to specific truck failures. The results indicate that the SPM method is able to detect machine failures of trucks with high accuracy with an average 96%. The proposed methodology may reduce the maintenance time, and the expenditures caused by truck breakdowns in the mining industry.",

keywords = "Mine equipment, Mining trucks, Predictive maintenance, Sequential pattern mining",

author = "Abdulgani Kahraman and Mehmed Kantardzic and Kahraman, {M. Mustafa} and Muhammed Kotan",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 1st Mediterranean Forum - Data Science Conference, MeFDATA 2020 ; Conference date: 24-10-2020 Through 24-10-2020",

year = "2021",

doi = "10.1007/978-3-030-72805-2_9",

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Kahraman, A, Kantardzic, M, Kahraman, MM & Kotan, M 2021, Sequential Pattern Mining Method for Predictive Maintenance of Large Mining Trucks. in J Hasic Telalovic & M Kantardzic (eds), Mediterranean Forum – Data Science Conference - First International Conference, MeFDATA 2020, Revised Selected Papers. Communications in Computer and Information Science, vol. 1343 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 126-136, 1st Mediterranean Forum - Data Science Conference, MeFDATA 2020, Virtual, Online, 24/10/20. https://doi.org/10.1007/978-3-030-72805-2_9

Sequential Pattern Mining Method for Predictive Maintenance of Large Mining Trucks. / Kahraman, Abdulgani; Kantardzic, Mehmed; Kahraman, M. Mustafa et al.
Mediterranean Forum – Data Science Conference - First International Conference, MeFDATA 2020, Revised Selected Papers. ed. / Jasminka Hasic Telalovic; Mehmed Kantardzic. Springer Science and Business Media Deutschland GmbH, 2021. p. 126-136 (Communications in Computer and Information Science; Vol. 1343 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sequential Pattern Mining Method for Predictive Maintenance of Large Mining Trucks

AU - Kahraman, Abdulgani

AU - Kantardzic, Mehmed

AU - Kahraman, M. Mustafa

AU - Kotan, Muhammed

PY - 2021

Y1 - 2021

N2 - In recent decades, various solutions had been sought for reducing operating costs while increasing the production of minerals in mining operations. Equipment health monitoring technologies had been used for monitoring and increasing the availability of machines. However, the data obtained from these technologies had only been used for monitoring the equipment health, and not for the prediction of failures. In this paper, it was relied on alarms and signals collected through real-time health monitoring technologies for predicting crucial mining truck failures. Sequential Pattern Mining (SPM) Method for Predictive Maintenance had been developed and implemented as a methodology to discover which group of alarms and signals might be related to specific truck failures. The results indicate that the SPM method is able to detect machine failures of trucks with high accuracy with an average 96%. The proposed methodology may reduce the maintenance time, and the expenditures caused by truck breakdowns in the mining industry.

AB - In recent decades, various solutions had been sought for reducing operating costs while increasing the production of minerals in mining operations. Equipment health monitoring technologies had been used for monitoring and increasing the availability of machines. However, the data obtained from these technologies had only been used for monitoring the equipment health, and not for the prediction of failures. In this paper, it was relied on alarms and signals collected through real-time health monitoring technologies for predicting crucial mining truck failures. Sequential Pattern Mining (SPM) Method for Predictive Maintenance had been developed and implemented as a methodology to discover which group of alarms and signals might be related to specific truck failures. The results indicate that the SPM method is able to detect machine failures of trucks with high accuracy with an average 96%. The proposed methodology may reduce the maintenance time, and the expenditures caused by truck breakdowns in the mining industry.

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KW - Mining trucks

KW - Predictive maintenance

KW - Sequential pattern mining

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AN - SCOPUS:85107402755

SN - 9783030728045

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BT - Mediterranean Forum – Data Science Conference - First International Conference, MeFDATA 2020, Revised Selected Papers

A2 - Hasic Telalovic, Jasminka

A2 - Kantardzic, Mehmed

PB - Springer Science and Business Media Deutschland GmbH

T2 - 1st Mediterranean Forum - Data Science Conference, MeFDATA 2020

Y2 - 24 October 2020 through 24 October 2020

ER -

Kahraman A, Kantardzic M, Kahraman MM, Kotan M. Sequential Pattern Mining Method for Predictive Maintenance of Large Mining Trucks. In Hasic Telalovic J, Kantardzic M, editors, Mediterranean Forum – Data Science Conference - First International Conference, MeFDATA 2020, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 126-136. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-72805-2_9

Sequential Pattern Mining Method for Predictive Maintenance of Large Mining Trucks

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Keywords

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