Multispectral visual detection method for conveyor belt longitudinal tear

Hou, Chengcheng; Qiao, Tiezhu; Zhang, Haitao; Pang, Yusong; Xiong, Xuejian

doi:10.1016/j.measurement.2019.05.010

Cited by 50 publications

(22 citation statements)

References 31 publications

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“…For the purposes of numerical research, four properties of particles and material were introduced: 1-density, 2-bulk density, 3-young's modulus, and 4-Poisson's ratio. It is constituted by static and dynamic friction and restitution coefficient for the correlation rubber-wood and rubber-steel [61,62] (Figure 7). During 10 seconds' simulation, mixed wood chips were supplied randomly to the conveyor belt in the quantities specified according to Table 2.…”

Section: Methodsmentioning

confidence: 99%

Computer Aided Modeling of Wood Chips Transport by Means of a Belt Conveyor with Use of Discrete Element Method

et al. 2020

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The effectiveness and precision of transporting wood chips on the transport trailer or hopper depends on an inclination angle, a conveyor belt speed, and length. In order to devise a methodology aiding designing and the selection of technical and performance parameters (aiding the settings of conveyor belt sub-assemblies), the authors carried out the simulation tests concerning wood chips transport on the belt conveyor and their outlet. For the purposes of these tests, a simulation model was performed in the Rocky DEM (discrete element method) software in the numerical analysis environment and compared to analytical tests. The tested wood chips were taken from cherry plum branches chipping processes (Prunus cerasifera Ehrh. Beitr. Naturk. 4:17. 1789 (Gartenkalender 4:189-204. 1784)), out of which seven basic fractions were separated, which differed mainly in terms of their diameter from 5 mm to 50 mm and the length of 150 mm. The article presents the results of wood chips ejection distance in the form of the 3D functions of wood chips ejection distance depending on the conveyor belt inclination angle and belt speed. The results are presented for five conveyor belt lengths (1 m, 2 m, 3 m, 4 m, 5 m). The tests also involved the conveyor belt inclination angle in the range from 10° to 50° and the belt velocity in the range from 1 m/s2 to 5 m/s2. The numerical test results demonstrate higher average values of wood chips ejection distance than designated in the analytical model. The average arithmetical difference in the results between the numerical and analytical model is at the level of 13%.

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Section: Methodsmentioning

confidence: 99%

Computer Aided Modeling of Wood Chips Transport by Means of a Belt Conveyor with Use of Discrete Element Method

et al. 2020

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“…Yu et al proposed a dual-band machine vision method based on the combination of medium infrared light and long infrared light to detect belt damage [12]. Hou et al employed the multi-spectral image acquisition sensor to decompose visible light into visible light, medium infrared light and far infrared light, and integrated the multi-spectral image for longitudinal tear detection of conveyor belt [13]. Qiao et al proposed an integrated binocular vision detection method on the basis of infrared and visible light fusion to detect the tearing of the mining conveyor belt [14].…”

Section: Introductionmentioning

confidence: 99%

Research On ADCN Method for Damage Detection of Mining Conveyor Belt

Qiao

Pang

et al. 2021

IEEE Sensors J.

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Belt conveyor is considered as a momentous component of modern coal mining transportation system, and thus it is an essential task to diagnose and monitor the damage of belt in real time and accurately. Based on the deep learning algorithm, this present study proposes a method of conveyor belt damage detection based on ADCN (Adaptive Deep Convolutional Network). A deep convolution network with unique adaptability is built to extract the different scale features of visible light image of conveyor belt damage, and the target is classified and located in the form of anchor boxes. A data set with data diversity is collected according to the actual working conditions of the conveyor belt. After training and regression, the ADCN model can perfectly capture and classify the damaged target in the video of the conveyor running. Compared with the SVM based method, the method based on ADCN can better meet the realtime and reliability requirements of belt damage detection, and it has the positioning function which SVM does not have.

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“…Further, if the damage of the conveyor belt cannot be detected and treated in time, it may eventually cause the occurrence of the conveyor belt tearing accident, which will cause greater harm. The current detection methods for conveyor belt tearing include: weak magnetic test [20], built-in sensor chip method [21], machine vision method [22][23][24][25], line laser-assisted method [26], infrared camera assisted method [27][28][29][30], audio-visual fusion method [31].Among them, most of the research work only focuses on the detection of tearing of conveyor belt, without the detection of other types of damage, which has a certain limitation.…”

Section: Introductionmentioning

confidence: 99%

Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt

Zhang

Zhou

et al. 2021

Applied Sciences

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Aiming at the problem that mining conveyor belts are easily damaged under severe working conditions, the paper proposed a deep learning-based conveyor belt damage detection method. To further explore the possibility of the application of lightweight CNNs in the detection of conveyor belt damage, the paper deeply integrates the MobileNet and Yolov4 network to achieve the lightweight of Yolov4, and performs a test on the exiting conveyor belt damage dataset containing 3000 images. The test results show that the lightweight network can effectively detect the damage of the conveyor belt, with the fastest test speed 70.26 FPS, and the highest test accuracy 93.22%. Compared with the original Yolov4, the accuracy increased by 3.5% with the speed increased by 188%. By comparing other existing detection methods, the strong generalization ability of the model is verified, which provides technical support and empirical reference for the visual monitoring and intelligent development of belt conveyors.

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Multispectral visual detection method for conveyor belt longitudinal tear

Cited by 50 publications

References 31 publications

Computer Aided Modeling of Wood Chips Transport by Means of a Belt Conveyor with Use of Discrete Element Method

Computer Aided Modeling of Wood Chips Transport by Means of a Belt Conveyor with Use of Discrete Element Method

Research On ADCN Method for Damage Detection of Mining Conveyor Belt

Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt

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