A new paradigm for intelligent status detection of belt conveyors based on deep learning

Zhang, Mengchao; Jiang, Kai; Cao, Yueshuai; Li, Meixuan; Wang, Qinbin; Liu, Dong; Zhang, Yuan

doi:10.1016/j.measurement.2023.112735

Cited by 18 publications

(6 citation statements)

References 35 publications

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“…Monitoring the condition of belts [12,56,57] and belt connections [8,9] is economically justified, especially when belts are replaced as a preventive measure [33,58] to be sent for refurbishment. There is also a growing interest in the application of artificial intelligence methods [59][60][61][62][63] in belt monitoring, damage prevention, and making better decisions regarding repairs and replacements.…”

Section: Methodsmentioning

confidence: 99%

Trends in the Growth of Damage Extents in a Steel Conveyor Belt’s Core

Jurdziak,

Błażej,

Kirjanów-Błażej

et al. 2024

Minerals

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The initial set of four measurements (scans) captures the failures occurring in the core of steel cable conveyor belt segments used in a lignite coal mine in Poland. These measurements utilize the DiagBelt+ magnetic system, enabling the analysis of averaged damage sizes. The scanning results allowed the analysis of complete changes in the failure sizes to a maximum of six different time intervals from 1 to 19 months. The cumulative increase in the length and width of the failure (their total extent in both directions) is a linear function of time. However, the inclination of the selected lines (growth speed) depends on the age of the belt segment at the time of its first scan. It is possible to find the best regression function of slope coefficients for the age of the belt segment. The obtained results are crucial for rational belt management, including reliable short-term forecasts of belt needs with the flow of time, indications of the best moments for belt segment replacements, and estimating the budget of the belting department.

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Section: Methodsmentioning

confidence: 99%

Trends in the Growth of Damage Extents in a Steel Conveyor Belt’s Core

Jurdziak,

Błażej,

Kirjanów-Błażej

et al. 2024

Minerals

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“…The typical design that defines the characteristics of the deposit of filtered tailings (dry stacking) is given by a conveyor belt that takes the filtered mine tailings from the filter press and deposits them in an intermediate fixed belt (transfer conveyor belt). Then, the cake is moved to a fixed distribution belt and finally to a mobile belt with a tripper car, which is in charge of the linear distribution of filtered mine tailings in the dry stacking zone (TSF) [50]. As a complementary activity, using a bulldozer is considered to distribute filtered mine tailings in the dry stacking zone (TSF).…”

Section: Filtered Mine Tailings Conveyance Systemmentioning

confidence: 99%

Dry Stacking of Filtered Tailings for Large-Scale Production Rates over 100,000 Metric Tons per Day: Envisioning the Sustainable Future of Mine Tailings Storage Facilities

Cacciuttolo,

Atencio

2023

Minerals

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Communities and authorities have been dismayed by globally recorded tailings storage facility (TSF) failures in recent years, which have negatively affected the safety of people and the integrity of the environment. In this context, obtaining the social and environmental license to operate TSFs has become a challenging process for mining companies. This has promoted the trend of using mine tailings dewatering technologies in the mining industry, with dry stacking of filtered mine tailings being recognized worldwide as one of the most acceptable, safe, and environmentally friendly solutions. This article presents a new paradigm in managing mine tailings, with disruptive and futuristic characteristics, considering the dry stacking of filtered mine tailings for large-scale industrial production rates over 100,000 metric tons per day (mtpd). Aspects of filtered tailings management are discussed, such as (i) dewatering process plant with thickening/filtering equipment, (ii) conveyance using fixed and movable conveyor belts, (iii) construction of dry stacking of filtered mine tailings facility, and (iv) implementation of Industry 4.0 technologies for automation of the mining processes. Finally, the article discusses how the large-scale filtered mine tailings solution is applied, considering the advances in the equipment’s performance and implementation of Industry 4.0 technologies as well as the experience gained worldwide in several mining operations. The future global trend is that mining operations with high daily production of mine tailings will apply dry stacking technology without dams to guarantee sustainability, promote continuity of the mining business, ensure the safety of communities, and conserve the environment.

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“…Conveyor belt torsion is commonly characterized by the rotation of the belt's lap position, exceeding an angle of 20° in either the clockwise or counterclockwise direction in relation to the structural frame of the conveyor [4]. At present, the detection of conveyor belt torsion may be categorized into two distinct approaches: the conventional method and the utilization of video-based artificial intelligence (AI) recognition technology [5][6].…”

Section: Introductionmentioning

confidence: 99%

A Machine Vision-Based Edge Detection Method for Belt Lap of Pipe Belt Conveyor

Hao,

Wang,

Zhang

et al. 2024

Advances in Transdisciplinary Engineering

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The extraction of positional features from the longitudinal lap edge straight line of the conveyor belt in a pipe belt conveyor holds significant importance in the detection of conveyor belt torsion faults. This study presents a proposed algorithm for detecting the longitudinal lap edge on a conveyor belt using machine vision. The algorithm involves several steps, including extracting regions of interest (ROI) from conveyor belt images and converting them to grayscale. Local enhancement is achieved through image stretching, and edge features are extracted using the Prewitt operator. Morphological methods are then applied to address impurities, and the lap edge is detected through straight line detection using the Hough transform. The conducted experiments involving the utilization of OpenCV on authentic images of a cylindrical pipe belt conveyor within an industrial setting demonstrate the algorithm’s proficient ability to accurately recognize the longitudinal lap edges of conveyor belts.

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A new paradigm for intelligent status detection of belt conveyors based on deep learning

Cited by 18 publications

References 35 publications

Trends in the Growth of Damage Extents in a Steel Conveyor Belt’s Core

Trends in the Growth of Damage Extents in a Steel Conveyor Belt’s Core

Dry Stacking of Filtered Tailings for Large-Scale Production Rates over 100,000 Metric Tons per Day: Envisioning the Sustainable Future of Mine Tailings Storage Facilities

A Machine Vision-Based Edge Detection Method for Belt Lap of Pipe Belt Conveyor

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