ORIGINAL ARTICLE
Feasibility Study of Neural Network-based Classification of Conveyor Belt Damage Using Partial DiagBelt Data
1
Wroclaw University of Science and Technology, Faculty of Geoengineering, Mining and Geology, Department of Mining, Wroclaw, Poland
These authors had equal contribution to this work
Submission date: 2024-12-03
Final revision date: 2025-06-02
Acceptance date: 2025-06-04
Online publication date: 2025-07-28
Publication date: 2025-07-28
Corresponding author
Aleksandra Rzeszowska
Faculty of Geoengineering, Mining and Geology, Department of Mining, Wrocław University of Science and Technology, na Grobli 15, 50-421, Wrocław, Poland
Faculty of Geoengineering, Mining and Geology, Department of Mining, Wrocław University of Science and Technology, na Grobli 15, 50-421, Wrocław, Poland
Civil and Environmental Engineering Reports 2025;35(3):313-325
KEYWORDS
TOPICS
ABSTRACT
Non-invasive methods for diagnosing conveyor belts enable effective detection of damage, significantly reducing the costs associated with belt replacement. Additionally, they allow for continuous monitoring of the belts’ technical condition and degree of wear over extended periods of operation. Such solutions also enhance safety in environments where conveyor systems are used. While belt wear is an inevitable process, its rate can vary depending on specific operational conditions, such as the conveyor’s location, its length, the type of material being transported, and the belt’s operating speed. This article discusses an artificial intelligence-based approach to classifying conveyor belt damage. A two-layer neural network was implemented in the MATLAB environment using the Deep Learning Toolbox. By optimizing the network, a high level of operational efficiency was achieved, reaching an accuracy range of 80–90%. This solution opens new possibilities for precise diagnostics and monitoring of conveyor belts’ technical state, contributing to improved durability and reduced operational costs.
REFERENCES (24)
1.
Webb, C, Sikorska, J, Khan, RN and Hodkiewicz, M 2020. Developing and evaluating predictive conveyor belt wear models. Data-Centric Engineering 1, e3.
2.
Żur, T and Hardygóra, M 1996. Przenośniki taśmowe w górnictwie [Conveyor belts in mining]. Katowice: Śląsk.
3.
Błażej, R 2018. Ocena stanu technicznego taśm przenośnikowych z linkami stalowymi [Assessment of the technical condition of steel cord conveyor belts]. Wrocław: Wydział Geoinżynierii, Górnictwa i Geologii Politechniki Wrocławskiej.
4.
Bugaric, U, Tanasijevic, M, Polovina, D, Ignjatovic, D and Jovancic, P 2012. Lost production costs of the overburden excavation system caused by rubber belt failure. Eksploatacja i Niezawodnosc 14.
5.
Kumbhar, SG and Edwin Sudhagar, P 2021. Fault Diagnostics of Roller Bearings Using Dimension Theory. Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems 4.
6.
Skoczylas, A, Stefaniak, P, Anufriiev, S and Jachnik, B 2021. Belt conveyors rollers diagnostics based on acoustic signal collected using autonomous legged inspection robot. Applied Sciences (Switzerland) 11.
7.
Roskosz, M, Złocki, A and Kwaśniewski, J 2020. Self Magnetic Flux Leakage as a Diagnostic Signal in the Assessment of Active Stress - Analysis of Influence Factors. Acta Physica Polonica A 137, 690–692.
8.
Zhang, M, Shi, H, Yu, Y and Zhou, M 2020. A Computer Vision Based Conveyor Deviation Detection System. Applied Sciences 10, 2402.
9.
Siami, M, Barszcz, T, Wodecki, J and Zimroz, R 2022. Design of an Infrared Image Processing Pipeline for Robotic Inspection of Conveyor Systems in Opencast Mining Sites. Energies 15, 6771.
10.
Martínez-Parrales, R and Téllez-Anguiano, AdC 2022. Vibration-based Fault Detection System with IoT Capabilities for a Conveyor Machine. Acta Polytechnica Hungarica 19.
11.
Błażej, R, Jurdziak, L, Kirjanów-Błazej, A and Kozłowski, T 2021. Identification of damage development in the core of steel cord belts with the diagnostic system. Scientific Reports 11.
12.
Fedorko, G 2019. Implementation of Industry 4.0 in the belt conveyor transport. MATEC Web of Conferences 263.
13.
Mendes, D, Gaspar, PD, Charrua-Santos, F and Navas, H 2023. Enhanced real-time maintenance management model - a step toward Industry 4.0 through Lean: Conveyor belt operation case study. Electronics 12.
14.
Błażej, R, Jurdziak, L, Kozłowski, T and Kirjanów, A 2018. The use of magnetic sensors in monitoring the condition of the core in steel cord conveyor belts – Tests of the measuring probe and the design of the DiagBelt system. Measurement: Journal of the International Measurement Confederation 123.
16.
Olchówka, D, Błażej, R and Jurdziak, L 2022. Selection of measurement parameters using the DiagBelt magnetic system on the test conveyor. Journal of Physics: Conference Series 2198, 012042.
17.
Haykin, S 2009. Neural Networks and Learning Machines, 3rd ed. Hamilton: Pearson Education, Inc.
18.
Rosenblatt, F 1958. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65, 386–408.
19.
Minsky, M and Papert, S 1969. Perceptrons: An Introduction to Computational Geometry, 1st ed. Cambridge, MA: MIT Press.
20.
Rumelhart, DE, Hinton, GE and Williams, RJ 1986. Learning representations by back-propagating errors. Nature 323, 533–536.
22.
Kim, J-W and Park, S 2018. Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation. Sensors 18, 109.
23.
Kirjanów-Błażej, A and Rzeszowska, A 2021. Conveyor belt damage detection with the use of a two-layer neural network. Applied Sciences (Switzerland) 11.
24.
Rzeszowska, A, Jurdziak, L, Błażej, R and Kirjanów-Błażej, A 2023. Application of Clustering and SOM Analysis for Identification of Conveyor Belt Damage Based on Data from the DiagBelt+ Magnetic System. In: Burduk, A., Batako, A., Machado, J., Wyczółkowski, R., Antosz, K., Gola, A. (eds) Advances in Production. ISPEM 2023. Lecture Notes in Networks and Systems, vol 790. Springer, Cham.
Share
RELATED ARTICLE
| eISSN: | 2450-8594 |
| ISSN: | 2080-5187 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
