Skin-on, and bone-in bellies (n = 94) were cut into Canadian specifications and assessed on an automated conveyor belt system based on different levels of firmness. Temperature settings at 4 °C, 2 °C, and - 1.5 °C had significant effect (P < 0.05) on the bending angle, after 24 cm of the belly had passed the nosebar. The stepwise regression relationship had R ∼ 0.18-0.67 between iodine value and bending angle at all temperatures. Bending bellies multiple times changed firmness classification of bellies at 4 and 2 °C, but bend number did not influence firmness classification at -1.5 °C. The automated conveyer belt system presented the potential to classify pork bellies based on firmness for industrial applications.
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http://dx.doi.org/10.1016/j.meatsci.2023.109222 | DOI Listing |
Sci Rep
January 2025
School of Intelligent Manufacturing and Modern Industry (School of Mechanical Engineering), Xinjiang University, Ürümqi, 830017, China.
The rapid expansion of the coal mining industry has introduced significant safety risks, particularly within the harsh environments of open-pit coal mines. The safe and stable operation of belt conveyor idlers is crucial not only for ensuring efficient coal production but also for safeguarding the lives of coal mine workers. Therefore, this paper proposes a method based on deep learning for real-time detection of conveyor idler faults.
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January 2025
School of Mechanical Engineering, Xinjiang University, Urumqi, 830000, China.
Real-time detection of conveyor belt tearing is of great significance to ensure mining in the coal industry. The longitudinal tear damage problem of conveyor belts has the characteristics of multi-scale, abundant small targets, and complex interference sources. Therefore, in order to improve the performance of small-size tear damage detection algorithms under complex interference, a visual detection method YOLO-STOD based on deep learning was proposed.
View Article and Find Full Text PDFMin Metall Explor
November 2024
Miller Consulting, Spokane, WA, USA.
Occupational exposures to respirable dusts and respirable crystalline silica (RCS) is well established as a health hazard in many industries including mining, construction, and oil and gas extraction. The U.S.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.
Belt conveyor idlers are freely rotating idlers supporting the belt of a conveyor, and can induce severe frictional damage to the belt as they fail. Therefore, fast and accurate detection of idler faults is crucial for the effective maintenance of belt conveyor systems. In this article, we implement and evaluate the performance of an idler stall detection system based on a multivariate deep learning model using accelerometers and microphone sensor data.
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December 2024
College of Safety Science and Engineering, Liaoning Technical University, 47 Zhonghua Road, Xihe District, Fuxin City, 123000, Liaoning Province, China.
Based on the engineering example of Linsheng coal mine, this paper uses TF1M3D computer simulation platform to systematically analyze the process of smoke flow spreading and air flow disorder disaster from the perspective of the whole mine network, and puts forward corresponding plans and measures. A small scale similar experiment was carried out to study the updraft flow fire in the mine. Through the analysis of the collected experimental data, the variation law of the air volume of the fire source in the main air path, side branch road and total air path with different air volume and the variation characteristics of the temperature at the monitoring point with time were obtained under different air volume conditions, and the critical air volume was fitted as 1.
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