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Image of Vibration Testing and System Dynamics
ISSN: 2475-4811 (print)
ISSN: 2475-482X (online)
Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland

Email: pawel.olejnik@p.lodz.pl

C. Steve Suh (editor)

Texas A&M University, USA

Email: ssuh@tamu.edu

Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China

Email: tuoxianguo@suse.edu.cn

Coal Flow Detection of Belt Conveyor Based on FPN

Journal of Vibration Testing and System Dynamics 10(3) (2026) 283--296 | DOI:10.5890/JVTSD.2026.09.006

Jinyang Zhang$^{1,2}$, Yilin Bei$^{3}$, Hao Wu$^{1,2}$

$^{1}$ School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, 644000, China

$^{2}$ Intelligent Perception and Control Key Laboratory of Sichuan Province, Yibin, 644000, China

$^{3}$ Taishan College Institute of Information Science and Technology, Taian, 271000, China

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Abstract

In coal mine production, real-time monitoring of coal flow on conveyor belts is of great significance for ensuring production safety and improving transportation efficiency. Existing coal flow detection methods primarily rely on parameters such as coal width, belt speed, and coal flow depth, employing volume modeling or mass estimation. These methods face challenges such as complex sensor deployment and high costs. Therefore, this paper proposes a coal flow detection method based on a Feature Pyramid Network (FPN), which models the dynamic changes in coal flow on the conveyor belt and determines the duration to achieve intelligent coal flow detection. The backbone network incorporates Partitioned Multi-head Self-Attention (PMSA) to enhance local modeling capabilities. The FPN structure includes Adaptive Fine-Grained Channel Attention (FCA) modules and Convolutional Block Attention Module (CBAM), effectively preventing information loss and enhancing responsiveness to critical spatiotemporal information. Experimental results demonstrate that this method achieves good detection performance across various scenarios, providing a decision-making basis for intelligent speed control of conveyor belts.

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