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

Hou, Chengcheng; Qiao, Tiezhu; Qiao, Maoying; Xiong, Xuejian; Yang, Yi; Zhang, Haitao

doi:10.1109/access.2019.2937660

Cited by 20 publications

(11 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three types of surface damages, i.e., scratch, crack and tear, are detected with SVM classifiers. Hou et al [ 30 ] proposed an improved image segmentation and morphological operation algorithm with aided audio analysis to detect conveyor belt tearing regions. The region growing algorithm was applied to the potential defect parts, and the segmentation result, combined with processed audio information, was examined.…”

Section: Defect Detection For Conveyor Beltsmentioning

confidence: 99%

“…Conveyor belt tear detection methods based on machine vision have developed from a singular reliance on visible light to a combination of visible light [ 27 , 28 , 29 , 31 ], hyperspectral [ 20 , 21 , 22 , 34 ], multispectral [ 23 , 24 ] and even audio-assisted [ 30 ] methods. No matter which methods are proposed, similar threshold algorithms are adopted in the image preprocessing step.…”

Section: Challenges and Solutionsmentioning

confidence: 99%

See 1 more Smart Citation

Belt Tear Detection for Coal Mining Conveyors

et al. 2022

View full text Add to dashboard Cite

The belt conveyor is the most commonly used conveying equipment in the coal mining industry. As the core part of the conveyor, the belt is vulnerable to various failures, such as scratches, cracks, wear and tear. Inspection and defect detection is essential for conveyor belts, both in academic research and industrial applications. In this paper, we discuss existing techniques used in industrial production and state-of-the-art theories for conveyor belt tear detection. First, the basic structure of conveyor belts is discussed and an overview of tear defect detection methods for conveyor belts is studied. Next, the causes of conveyor belt tear are classified, such as belt aging, scratches by sharp objects, abnormal load or a combination of the above reasons. Then, recent mainstream techniques and theories for conveyor belt tear detection are reviewed, and their characteristics, advantages and shortcomings are discussed. Furthermore, image dataset preparation and data imbalance problems are studied for belt defect detection. Moreover, the current challenges and opportunities for conveyor belt defect detection are discussed. Lastly, a case study was carried out to compare the detection performance of popular techniques using industrial image datasets. This paper provides professional guidelines and promising research directions for researchers and engineers based on the leading theories in machine vision and deep learning.

show abstract

Section: Defect Detection For Conveyor Beltsmentioning

confidence: 99%

Section: Challenges and Solutionsmentioning

confidence: 99%

Belt Tear Detection for Coal Mining Conveyors

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In addition, with the growth of productivity and development of the automation systems in mining enterprises, environmental monitoring systems have appeared [14]. In particular, to increase the conveyor belts service life due to their timely repair, the systems to monitor the condition of conveyor belts came to operation, which should record their damage, including longitudinal tear [15].…”

Section: Electromagnetic Interactionmentioning

confidence: 99%

Analysis of Devices to Detect Longitudinal Tear on Conveyor Belts

et al. 2020

View full text Add to dashboard Cite

Currently mining enterprises more and more often use belt conveyors for mining operations. The length of transporting rock mass could be several kilometers, so in most cases conveyor are used. The transported rock mass is rather heterogeneous, as a rule, it has inclusions in a form of large pieces with sharp edges and, in some cases, metal objects, which might cause longitudinal tears of various lengths on conveyor belts. They lead to significant repair costs or replacement of expensive conveyor belt, loss of transported material, and downtime of the major enterprise equipment. In the world practice various devices and systems to detect longitudinal tears on conveyor belts have been developed for their successful operation. In this paper, there is analysis of existing devices to detect longitudinal tears on conveyor belts based on the use of mechanical (detection of the transported cargo on the lower the conveyor section and measuring the belt width), ultrasonic and electro-magnetic energy, evaluation of changes in the magnetic field configuration, computer vision, infrared laser and x-ray radiation, image recognition software and embedded transponder chips.

show abstract

“…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]. However, due to its imaging principle, the method based on infrared image still has great limitations such as high noise, high cost and poor effect in the actual experiment.…”

Section: Introductionmentioning

confidence: 99%

Research On ADCN Method for Damage Detection of Mining Conveyor Belt

Qiao

Pang

et al. 2021

IEEE Sensors J.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 20 publications

References 33 publications

Belt Tear Detection for Coal Mining Conveyors

Belt Tear Detection for Coal Mining Conveyors

Analysis of Devices to Detect Longitudinal Tear on Conveyor Belts

Research On ADCN Method for Damage Detection of Mining Conveyor Belt

Contact Info

Product

Resources

About