Predictive Modeling of Conveyor Belt Deterioration in Coal Mines Using AI Techniques

Parthkumar Parmar, Leszek Jurdziak (), Aleksandra Rzeszowska and Anna Burduk
Additional contact information
Parthkumar Parmar: Department of Laser Technologies, Automation and Production Organization, Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, ul. Łukasiewicza 5, 50-371 Wrocław, Poland
Leszek Jurdziak: Department of Mining, Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, ul. Na Grobli 15, 50-421 Wrocław, Poland
Aleksandra Rzeszowska: Department of Mining, Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, ul. Na Grobli 15, 50-421 Wrocław, Poland
Anna Burduk: Department of Laser Technologies, Automation and Production Organization, Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, ul. Łukasiewicza 5, 50-371 Wrocław, Poland

Energies, 2024, vol. 17, issue 14, 1-30

Abstract: Conveyor belts are vital for material transportation in coal mines due to their cost-effectiveness and versatility. These belts endure significant wear from harsh operating conditions, risking substantial financial losses if they fail. This study develops five artificial neural network (ANN) models to predict conveyor belt damage using 11 parameters from the Belchatow brown coal mine in Poland. The models target five outputs: number of repairs and cable cuts, cumulative number of repairs and cable cuts, and their ages. Various optimizers (Adam, Nadam, RMSprop, Adamax, and stochastic gradient descent or SGD) and activation functions (ReLU, Swish, sigmoid, tanh, Leaky ReLU, and softmax) were tested to find the optimal configurations. The predictive performance was evaluated using three error indicators against actual mine data. Superior models can forecast belt behavior under specific conditions, aiding proactive maintenance. The study also advocates for the Diagbelt+ system over human inspections for failure detection. This modeling approach enhances proactive maintenance, preventing total system breakdowns due to belt wear.

Keywords: coal mining; conveyor belt systems; wear; service life; artificial neural network; proactive maintenance (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: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/14/3497/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/14/3497/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:14:p:3497-:d:1436422

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().