PPIM: A point-by-point interpolation method for breakpoint connection used for load detection of belt conveyor

Zhang, Mengchao; Zhang, Dongyue; Chào, Y.; Li, Meixuan; Liu, Luxuan; Xue, Mingyuan; Hao, Nini; Zhang, Yuan

doi:10.1016/j.measurement.2022.112125

Cited by 2 publications

(3 citation statements)

References 32 publications

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“…Area ratio method 32 Simple but practical The height of the material is ignored, which belongs to the fuzzy measurement method Line-laser-assisted vision method (laser triangulation method) 28,29,48 High measurement accuracy There is a breakpoint and disconnection problem of the laser line that needs to be solved Deep-learning-based classification method 49 Simple and practical, only need to replace the corresponding dataset to complete Manually labeled data are required, which are greatly affected by human subjective factors…”

Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

“…Commonly used networks include the improved single-shot detector (SSD), 23 U-net, 24 You Only Look Once (Yolo), 25,26 and temporal convolutional networks. 27 For load measurement, the mainstream methods are the optical measurement method 28,29 and the laser point cloud-based measurement method. 28,29 All of these methods have proven to be effective.…”

Section: Introductionmentioning

confidence: 99%

“…27 For load measurement, the mainstream methods are the optical measurement method 28,29 and the laser point cloud-based measurement method. 28,29 All of these methods have proven to be effective. The research focus in this field is gradually shifting toward building more comprehensive datasets and developing more efficient and robust detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Deep-learning-based multistate monitoring method of belt conveyor turning section

Zhang,

Jiang,

Zhao

et al. 2023

Structural Health Monitoring

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During transportation, bulk materials are susceptible to spillage due to equipment instability and environmental factors, resulting in increased maintenance costs and environmental pollution. Thus, intelligent and efficient condition monitoring is crucial for maintaining operational efficiency of transfer equipment. It facilitates the timely identification of potential safety hazards, preventing accidents from occurring or their impact from spreading, thereby minimizing production and maintenance costs. This study presents a deep-learning-based multioperation synchronous monitoring method suitable for belt conveyors that integrate target segmentation and detection networks to simultaneously diagnose belt deviation, measure conveying load, identify idlers, and do other tasks on a self-made dataset. This method effectively reduces the complexity of multistate simultaneous monitoring and monitoring costs, thereby avoiding environmental pollution caused by transportation accidents. Experimental results show that the segmentation accuracy of the proposed method can be up to 88.72%, with a detection accuracy of 91.3% and an overall inference speed of 90.9 frames per second. Furthermore, by extending the dataset, the proposed method can incorporate additional tasks, such as belt damage, scattered material, and foreign object identifications. This study has practical significance in ensuring the normal and eco-friendly operation of bulk material transportation. Our source dataset is available at https://github.com/zhangzhangzhang1618/dataset-for-turnning-section

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Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep-learning-based multistate monitoring method of belt conveyor turning section

Zhang,

Jiang,

Zhao

et al. 2023

Structural Health Monitoring

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Energy Balance of the Dynamic Impact Stressing of Conveyor Belts

2023

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This paper presents research into the energy balance of stressing conveyor belts by impact processes. Research is described in the areas of measuring the resistance of conveyor belts to puncture, the impact process, and the effect of changes to the impact height on the tensile and impact forces. Measurements were performed on a rubber–textile belt with a polyamide carcass. Hammers with pyramidal and spherical impactors were used to compare the resistance of the examined conveyor. Values were obtained for the maximum energies for the selected impact heights and the residual positional energies for the measured bounce heights. The difference between these energies represents the impact energy. The results confirmed that the impact energy of a hammer with a pyramidal impactor is greater than the impact energy of a hammer with a spherical impactor, while this difference increases with increasing impact height. The shape of the pyramidal impactor simulates sharp-edged materials, which is the cause of greater damage in the tribological interaction with the conveyor belt. This paper provides information for determining the limit conditions for setting appropriate impact heights depending on the weight of the material transported using a given belt conveyor.

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PPIM: A point-by-point interpolation method for breakpoint connection used for load detection of belt conveyor

Cited by 2 publications

References 32 publications

Deep-learning-based multistate monitoring method of belt conveyor turning section

Deep-learning-based multistate monitoring method of belt conveyor turning section

Energy Balance of the Dynamic Impact Stressing of Conveyor Belts

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