On-line intelligent evaluation of the fatigue state of a composite conveyor belt

Mei, Xiuzhuang; Miao, Changyun; Yang, Yuqiang; Li, Xianguo

doi:10.2298/tsci200216105m

THERM SCI

2021

DOI: 10.2298/tsci200216105m

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On-line intelligent evaluation of the fatigue state of a composite conveyor belt

Xiuzhuang Mei

Changyun Miao

Yuqiang Yang

et al.

Abstract: Fatigue failure of a composite conveyor belt occurs frequently, which will finally lead to an accident. An adaptive segmentation model is established to identify the rubber surface?s fatigue failure and to evaluate the belt fatigue level by using the machine vision technology and the artificial bee colony algorithm. The results show that the damage rate of rubber surface can be correctly calculated and the fatigue failure state can be objectively evaluated.

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Cited by 2 publications

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Study of conveyor belt deviation detection based on improved YOLOv8 algorithm

Ni,

Cheng,

Hou

et al. 2024

Sci Rep

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Conveyor belt deviation is a commmon and severe type of fault in belt conveyor systems, often resulting in significant economic losses and potential environment pollution. Traditional detection methods have obvious limitations in fault localization precision and analysis accuracy, unable to meet the demands of efficient and real-time fault detection in complex industrial scenarios. To address these issues, this paper proposes an improved detection algorithm based on YOLOv8, aiming to achieve efficient and accurate detection during the operation of the belt. Firstly, an Enhanced Squeeze-and-Excitation (ESE) module is incorporated into C2f to boost feature extraction for rollers and belts. Secondly, the construction of the BiFPN_DoubleAttention module in the neck network enhances bidirectional feature fusion and attention mechanism, thereby improving multi-scale object localization accuracy under complex environments. Then, a Multi-Head Self-Attention (MHSA) mechanism is introduced in the head network, better capturing positional features of small roller targets and belt areas in various environments, thus enhancing detection performance. Finally, extensive experiments are conducted on a self-built dataset, achieving an accuracy of 98.1%, mAP0.5 of 99.0%, and a detection speed of 46 frames per second (FPS). This method effectively handles variations and disturbances in the conveyor belt transportation environment, meeting real-time diagnostic needs for belt misalignment in the industry.

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Study of conveyor belt deviation detection based on improved YOLOv8 algorithm

Ni,

Cheng,

Hou

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

An on-line detection method for conveyor belt deviation faults

et al. 2023

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The conveyor belt deviation occurs frequently, and it will finally lead to an accident, so its detection has triggered skyrocketing attention from both industry and academia. In this paper, an adaptive segmentation model and a belt offset quantification model are established for continuous online detection of the conveyor belt deviation status. The results show that the degree of the conveyor belt deviation can be quantitatively calculated and its deviation status can be objectively evaluated. This technology has opened the path for a new way to on-line continuously detect the conveyor belt deviation.

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On-line intelligent evaluation of the fatigue state of a composite conveyor belt

Cited by 2 publications

References 10 publications

Study of conveyor belt deviation detection based on improved YOLOv8 algorithm

Study of conveyor belt deviation detection based on improved YOLOv8 algorithm

An on-line detection method for conveyor belt deviation faults

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