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Volume 22
Issue 9
10.3390/s22093485
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Article

Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network

by
Xiaoqiang Guo
1,
Xinhua Liu
1,*,
Grzegorz Królczyk
2,
Maciej Sulowicz
3,
Adam Glowacz
3,
Paolo Gardoni
4 and
Zhixiong Li
2,5
1
School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 211006, China
2
Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland
3
Department of Electrical Engineering, Cracow University of Technology, 31-155 Cracow, Poland
4
Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
5
Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(9), 3485; https://doi.org/10.3390/s22093485
Submission received: 12 April 2022 / Revised: 23 April 2022 / Accepted: 27 April 2022 / Published: 3 May 2022
(This article belongs to the Special Issue Sensors and Signal Processing for Fault Diagnosis and Failure Prognosis of Means of Transport)
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Abstract

The belt conveyor is an essential piece of equipment in coal mining for coal transportation, and its stable operation is key to efficient production. Belt surface of the conveyor is vulnerable to foreign bodies which can be extremely destructive. In the past decades, much research and numerous approaches to inspect belt status have been proposed, and machine learning-based non-destructive testing (NDT) methods are becoming more and more popular. Deep learning (DL), as a branch of machine learning (ML), has been widely applied in data mining, natural language processing, pattern recognition, image processing, etc. Generative adversarial networks (GAN) are one of the deep learning methods based on generative models and have been proved to be of great potential. In this paper, a novel multi-classification conditional CycleGAN (MCC-CycleGAN) method is proposed to generate and discriminate surface images of damages of conveyor belt. A novel architecture of improved CycleGAN is designed to enhance the classification performance using a limited capacity images dataset. Experimental results show that the proposed deep learning network can generate realistic belt surface images with defects and efficiently classify different damaged images of the conveyor belt surface.
Keywords: damage detection; conditional CycleGAN; incremental image fusion; transfer learning damage detection; conditional CycleGAN; incremental image fusion; transfer learning

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MDPI and ACS Style

Guo, X.; Liu, X.; Królczyk, G.; Sulowicz, M.; Glowacz, A.; Gardoni, P.; Li, Z. Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network. Sensors 2022, 22, 3485. https://doi.org/10.3390/s22093485

AMA Style

Guo X, Liu X, Królczyk G, Sulowicz M, Glowacz A, Gardoni P, Li Z. Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network. Sensors. 2022; 22(9):3485. https://doi.org/10.3390/s22093485

Chicago/Turabian Style

Guo, Xiaoqiang, Xinhua Liu, Grzegorz Królczyk, Maciej Sulowicz, Adam Glowacz, Paolo Gardoni, and Zhixiong Li. 2022. "Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network" Sensors 22, no. 9: 3485. https://doi.org/10.3390/s22093485

APA Style

Guo, X., Liu, X., Królczyk, G., Sulowicz, M., Glowacz, A., Gardoni, P., & Li, Z. (2022). Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network. Sensors, 22(9), 3485. https://doi.org/10.3390/s22093485

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