Longitudinal tear early-warning method for conveyor belt based on infrared vision

Yang, Yi; Hou, Chengcheng; Qiao, Tiezhu; Zhang, Haitao; Ma, Ling

doi:10.1016/j.measurement.2019.07.045

Cited by 24 publications

(15 citation statements)

References 23 publications

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“…Pre-emphasis can enhance the spectrum of the falling part of the sound signals, thereby maintaining the flatness of the signals spectrum and preparing for the subsequent spectrum analysis work [27]. Preemphasis is usually implemented by using a first-order digital filter as shown in (2).…”

Section: ) Pre-emphasismentioning

confidence: 99%

“…Conveyor belts are one of the important equipment for coal mine production, which are widely used in coal production and transportation. Due to the influence of the materials transported, such as gangue, anchors, etc., the conveyor belts are prone to longitudinal tear [1], [2]. Once the conveyor belt longitudinal tear occurs, it will seriously affect the production of coal mine and even cause personal injury.…”

Section: Introductionmentioning

confidence: 99%

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Research on Audio-Visual Detection Method for Conveyor Belt Longitudinal Tear

Hou

Qiao

et al. 2019

IEEE Access

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As a key equipment for production and transportation, conveyor belts are widely used in the coal mining industry. Once the conveyor belt longitudinal tear occurs, it will seriously affect the production and even cause personal injury. Therefore, the longitudinal tear detection of the conveyor belt is extremely important. In this paper, the sound detection is introduced into the longitudinal tear detection of the conveyor belt, and an audio-visual detection method for conveyor belt longitudinal tear is proposed. Camera and microphone array are used to collect the image and sound signals of conveyor belt, and the conveyor belt is detected from both image and sound, respectively. Then the image and sound analysis results are combined to comprehensively judge the status of the conveyor belt. The experimental results show that the audiovisual detection method can accurately identify the normal, abnormal, and longitudinal tear of the conveyor belt. The detection accuracy is over 86.72% and the sensitivity of longitudinal tear detection is greater than 92.59%. The proposed audio-visual detection method is verified to meet the requirements of longitudinal tear detection of coal mining industry conveyor belts.

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Section: ) Pre-emphasismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Audio-Visual Detection Method for Conveyor Belt Longitudinal Tear

Hou

Qiao

et al. 2019

IEEE Access

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“…Therefore, it plays an extremely important role in the operation of belt conveyor. In the working process of the belt conveyor, harsh working environment and sudden accidents may lead to longitudinal tearing of conveyor belt [2] [3], which may lead to machine damage, production stagnation and even casualties in serious cases.…”

Section: Introductionmentioning

confidence: 99%

“…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]. Yang et al used the infrared light detection method based on the new infrared image binarization threshold automatic selection algorithm to detect the tear of conveyor belt [3]. Hou et al combined machine vision and GMM-UBM sound recognition to comprehensively detect the tearing of conveyor belt [15].…”

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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Longitudinal tear early-warning method for conveyor belt based on infrared vision

Cited by 24 publications

References 23 publications

Research on Audio-Visual Detection Method for Conveyor Belt Longitudinal Tear

Research on Audio-Visual Detection Method for Conveyor Belt Longitudinal Tear

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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