Research on Coal and Gangue Identification Method Based on Infrared Thermal Wave Detection

2013 ◽  
Vol 313-314 ◽  
pp. 1285-1287
Author(s):  
Yi Ding Zhao ◽  
Shao Peng Hu
Keyword(s):  
Thermal Wave ◽  
Detection Method ◽  
Waste Rock ◽  
Wave Detection ◽  
Identification Method ◽  
Detection Technology ◽  
Wave Image ◽  
Different Characteristics

A method based on infrared thermal wave to distinguish coal and waste rock is introduced. Infrared thermal wave detection technology, a fast and effective detection method, can detect different characteristics of coal and gangue on the surface or subsurface. Test showed that coal and waste rock can be distinguished effectively by processing their infrared thermal wave image.

Review on Key Technologies of Infrared Thermal Wave Detection Technology in Parts Defects

2012 ◽  
Vol 542-543 ◽  
pp. 814-817
Author(s):  
Jian Min Zhou ◽  
Li Juan Fan ◽  
Rui Feng Zhang
Keyword(s):  
Thermal Wave ◽  
Heat Transfer Analysis ◽  
Research Progress ◽  
Destructive Testing ◽  
Existing Problems ◽  
Wave Detection ◽  
Detection Technology ◽  
Key Technologies ◽  
New Type ◽  
Non Destructive

Infrared thermal wave detection technology has been found to be a new type of wide attention non-destructive testing (NDT) method due to its non-contact, non-destructive, rapidity, wide imaging field and advantages of direct observation. Key technologies and the latest research progress of the infrared thermal wave technology in parts defects are reviewed in this paper. The key technologies include the heat stimulator, infrared thermal image processing, heat transfer analysis and etc. The existing problems of the technology are also pointed out.

Research Situation Analysis of On-line Detection Technology for Power Transformer Winding Deformation

2020 ◽  
Vol 13 (6) ◽  
pp. 823-832
Author(s):  
Zhenhua Li ◽  
Weihui Jiang ◽  
Li Qiu ◽  
Zhenxing Li ◽  
Yanchun Xu
Keyword(s):  
Detection Method ◽  
Theoretical Research ◽  
Line Detection ◽  
Response Analysis ◽  
Advantages And Disadvantages ◽  
Detection Technology ◽  
The Future ◽  
Transformer Winding ◽  
On Line ◽  
Winding Deformation

Background: Winding deformation is one of the most common faults in power transformers, which seriously threatens the safe operation of transformers. In order to discover the hidden trouble of transformer in time, it is of great significance to actively carry out the research of transformer winding deformation detection technology. Methods: In this paper, several methods of winding deformation detection with on-line detection prospects are summarized. The principles and characteristics of each method are analyzed, and the advantages and disadvantages of each method as well as the future research directions are expounded. Finally, aiming at the existing problems, the development direction of detection method for winding deformation in the future is prospected. Results: The on-line frequency response analysis method is still immature, and the vibration detection method is still in the theoretical research stage. Conclusion: The ΔV − I1 locus method provides a new direction for on-line detection of transformer winding deformation faults, which has certain application prospects and practical engineering value.

scholarly journals A Two-Phase Fashion Apparel Detection Method Based on YOLOv4

2021 ◽  
Vol 11 (9) ◽  
pp. 3782
Author(s):  
Chu-Hui Lee ◽  
Chen-Wei Lin
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Detection Method ◽  
Phase Transfer ◽  
Recognition Task ◽  
Phase Detection ◽  
Target Domain ◽  
Two Phase ◽  
Detection Technology ◽  
Fashion Apparel

Object detection is one of the important technologies in the field of computer vision. In the area of fashion apparel, object detection technology has various applications, such as apparel recognition, apparel detection, fashion recommendation, and online search. The recognition task is difficult for a computer because fashion apparel images have different characteristics of clothing appearance and material. Currently, fast and accurate object detection is the most important goal in this field. In this study, we proposed a two-phase fashion apparel detection method named YOLOv4-TPD (YOLOv4 Two-Phase Detection), based on the YOLOv4 algorithm, to address this challenge. The target categories for model detection were divided into the jacket, top, pants, skirt, and bag. According to the definition of inductive transfer learning, the purpose was to transfer the knowledge from the source domain to the target domain that could improve the effect of tasks in the target domain. Therefore, we used the two-phase training method to implement the transfer learning. Finally, the experimental results showed that the mAP of our model was better than the original YOLOv4 model through the two-phase transfer learning. The proposed model has multiple potential applications, such as an automatic labeling system, style retrieval, and similarity detection.

scholarly journals A Test Device for Optimize PMU-Based Islanding Detection Technology

2015 ◽  
Vol 10 (2) ◽  
Keyword(s):  
Power Systems ◽  
Power Plants ◽  
Detection Method ◽  
Detection Methods ◽  
Islanding Detection ◽  
Algorithm Optimization ◽  
Test Device ◽  
Photovoltaic Power ◽  
Detection Technology ◽  
Bulk Power

Islanding detection is a necessary function for grid connected distributed generators. Usually, islanding detection methods can be classified as two catalogues: remote detecting methods and local detecting methods. Most of them have limitation and defects when they are applied in photovoltaic power stations. Recently synchronous phasor measuring units (PMU) is proposed to be applied for islanding detecting. Although the islanding detection method is supposed to be applied for traditional bulk power systems, it is also suitable for renewable generation power plants. To do this islanding detection will be implemented on central management unit of photovoltaic power station instead of on grid-tied inverters as traditionally. In implementing, the criteria of this method and the threshold of algorithm are needed to be optimized. This paper develops a test device which can optimize PMU-based islanding detection technology to validate the proposed islanding detection method applying in PV station. Then using simulation to discuss how to set a reasonable threshold for the researched islanding detection method applied in PV stations. Finally the paper provides a platform for the algorithm optimization.

scholarly journals The Rapid Detection Technology of Lamb Wave for Microcracks in Thin-Walled Tubes

2019 ◽  
Vol 9 (17) ◽  
pp. 3576 ◽  
Author(s):  
Yang ◽  
Wang ◽  
Yang
Keyword(s):  
Lamb Wave ◽  
Rapid Detection ◽  
Detection Method ◽  
Lamb Waves ◽  
Thin Plates ◽  
Depth Information ◽  
Amplitude Curve ◽  
Dispersion Characteristics ◽  
Detection Technology ◽  
Thin Walled

Thin-walled tubes are a kind of pressure vessel formed by a stamping and drawing process, which must withstand a great deal of sudden pressure during use. When microcrack defects of a certain depth are present on its inner and outer surfaces, severe safety accidents may occur, such as cracking and crushing. Therefore, it is necessary to carry out nondestructive testing of thin-walled tubes in the production process to eliminate the potential safety hazards. To realize the rapid detection of microcracks in thin-walled tubes, this study could be summarized as follows: (i) Because the diameters of the thin-walled tubes were much larger than their thicknesses, Lamb wave characteristics of plates with equal thicknesses were used to approximate the dispersion characteristics of thin-walled tubes. (ii) To study the dispersion characteristics of Lamb waves in thin plates, the detection method of the mode was determined using the particle displacement–amplitude curve. (iii) Using a multi-channel parallel detection method, rapid detection equipment for Lamb wave microcracks in thin-walled tubes was developed. (iv) The filtering peak values for defect signal detection with different depths showed that the defect detection peak values could reflect the defect depth information. (v) According to the minimum defect standard of a 0.045-mm depth, 100,000 thin-walled tubes were tested. The results showed that the missed detection rate was 0%, the reject rate was 0.3%, and the detection speed was 5.8 s/piece, which fully meets the actual detection requirements of production lines. Therefore, this study not only solved the practical issues for the rapid detection of microcracks in thin-walled tubes but also provided a reference for the application of ultrasonic technology for the detection of other components.

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