scholarly journals Detection Technology of Foamed Mixture Lightweight Soil Embankment Based on Ultrasonic Wave Transmission Method

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Shikun Pu ◽  
Baoning Hong ◽  
Xin Liu ◽  
Fenqiang Xu ◽  
Hao Shan
Keyword(s):  
Ultrasonic Wave ◽  
Detection Method ◽  
Wave Transmission ◽  
Simulation Approach ◽  
Transmission Method ◽  
Detection Technology ◽  
Ultrasonic Parameters ◽  
Test Distance ◽  
Quality Diagnosis ◽  
Curing Age

This study attempted to establish a process that uses the ultrasonic wave transmission method to correlate the ultrasonic parameters with the material properties of Foamed Mixture Lightweight Soil (FMLS). The results were then applied for the defect detection of the FMLS embankment. First, the ultrasonic wave velocity (UPV) and amplitude (UPA) of FMLS with different mix proportions were collected continuously from 3rd day to 45th day in the curing age. The relationships between UPV versus FMLS elastic modulus, unconfined compressive strength, and density were calibrated. The variations in the ultrasonic parameters owing to the test distance and crack width were recorded. Then, the laboratory tests were reproduced through numerical simulation approach. Finally, the reliability and accuracy of the proposed detection method for FMLS were proved and validated through on-site tests. The proposed methodology, which is simple, stable, and reliable, was found to be suitable for the quality diagnosis of FMLS embankments after construction and during operation.

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.

scholarly journals A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes

2019 ◽  
Vol 9 (13) ◽  
pp. 2771 ◽  
Author(s):  
Ping Zhou ◽  
Gongbo Zhou ◽  
Zhencai Zhu ◽  
Zhenzhi He ◽  
Xin Ding ◽  
...  
Keyword(s):  
Damage Detection ◽  
Detection Method ◽  
Steel Wire ◽  
Guided Wave ◽  
Detection Methods ◽  
Wire Ropes ◽  
Intelligent Sensors ◽  
Detection Technology ◽  
Damage States ◽  
Non Destructive

As an important load-bearing component, steel wire ropes (WRs) are widely used in complex systems such as mine hoists, cranes, ropeways, elevators, oil rigs, and cable-stayed bridges. Non-destructive damage detection for WRs is an important way to assess damage states to guarantee WR’s reliability and safety. With intelligent sensors, signal processing, and pattern recognition technology developing rapidly, this field has made great progress. However, there is a lack of a systematic review on technologies or methods introduced and employed, as well as research summaries and prospects in recent years. In order to bridge this gap, and to promote the development of non-destructive detection technology for WRs, we present an overview of non-destructive damage detection research of WRs and discuss the core issues on this topic in this paper. First, the WRs’ damage type is introduced, and its causes are explained. Then, we summarize several main non-destructive detection methods for WRs, including electromagnetic detection method, optical detection method, ultrasonic guided wave detection method, and acoustic emission detection method. Finally, a prospect is put forward. Based on the review of papers, we provide insight about the future of the non-destructive damage detection methods for steel WRs to a certain extent.

Research on Dynamic Intrusion Detection Model Based on Risk Coefficient

2010 ◽  
Vol 129-131 ◽  
pp. 124-127 ◽  
Author(s):  
Zheng Wei ◽  
Jun Yi Hou ◽  
Hua Tan ◽  
Guang Nan Guo
Keyword(s):  
Intrusion Detection ◽  
Detection Method ◽  
System Call ◽  
Security Technology ◽  
Detection Model ◽  
Risk Coefficient ◽  
Detection Technology ◽  
System Risk ◽  
Effectiveness And Efficiency ◽  
Definition Of

Intrusion detection technology is a kind of network security technology that can protect system from attacks. Based on the definition of system call risk coefficient, the paper brought out a system risk coefficient based dynamic intrusion detection model. Using the model, the drawbacks of traditional intrusion detection method based on system call was solved, which speeds up detection process and decreased false rate and error rate. It can also effectively identify error operations or users. The experiment result also proves the effectiveness and efficiency of the method.

scholarly journals Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

2020 ◽  
Vol 10 (7) ◽  
pp. 2443
Author(s):  
Huaitao Shi ◽  
Jin Guo ◽  
Xiaotian Bai ◽  
Lei Guo ◽  
Zhenpeng Liu ◽  
...  
Keyword(s):  
Fault Detection ◽  
Nonlinear Dynamic ◽  
Detection Method ◽  
Normal Operation ◽  
Rolling Bearings ◽  
Detection Delay ◽  
Detection Technology ◽  
Non Linear ◽  
Incipient Fault ◽  
Incipient Fault Detection

The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability.

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