scholarly journals Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 486
Author(s):  
Yiming Zhao ◽  
Jing Yan ◽  
Yanxin Wang ◽  
Qianzhen Jing ◽  
Tingliang Liu
Keyword(s):  
Crack Detection ◽  
Online Monitoring ◽  
Rapid Development ◽  
Mechanical Damage ◽  
Surface States ◽  
Detection Methods ◽  
Spectral Information ◽  
Porcelain Insulator ◽  
Term Operation ◽  
Porcelain Insulators

A porcelain insulator is an important part to ensure that the insulation requirements of power equipment can be met. Under the influence of their structure, porcelain insulators are prone to mechanical damage and cracks, which will reduce their insulation performance. After a long-term operation, crack expansion will eventually lead to breakdown and safety hazards. Therefore, it is of great significance to detect insulator cracks to ensure the safe and reliable operation of a power grid. However, most traditional methods of insulator crack detection involve offline detection or contact measurement, which is not conducive to the online monitoring of equipment. Hyperspectral imaging technology is a noncontact detection technology containing three-dimensional (3D) spatial spectral information, whereby the data provide more information and the measuring method has a higher safety than electric detection methods. Therefore, a model of positioning and state classification of porcelain insulators based on hyperspectral technology is proposed. In this model, image data were used to extract edges to locate cracks, and spectral information was used to classify the surface states of porcelain insulators with EfficientNet. Lastly, crack extraction was realized, and the recognition accuracy of cracks and normal states was 96.9%. Through an analysis of the results, it is proven that the crack detection method of a porcelain insulator based on hyperspectral technology is an effective non-contact online monitoring approach, which has broad application prospects in the era of the Internet of Things with the rapid development of electric power.

scholarly journals The Tunnel Structural Mode Frequency Characteristics Identification and Analysis Based on a Modified Stochastic Subspace Identification Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Biao Zhou ◽  
Xiongyao Xie ◽  
Xiaojian Wang
Keyword(s):  
Online Monitoring ◽  
Rapid Development ◽  
Health Assessment ◽  
Field Tests ◽  
Mode Frequency ◽  
Subspace Identification ◽  
Monitoring Methods ◽  
Underground Engineering ◽  
Stochastic Subspace Identification ◽  
Identification Method

With the rapid development of underground engineering in China, the heavy structural maintenance work followed is expected to be a great challenge in the future. The development also provides a promising application prospect for the newly developed vibration-based health assessment and monitoring methods. However, the fact that tunnels are embedded in soil makes collecting and identifying the vibration characteristics more difficult, especially for the online monitoring. In this paper, a new identification method that combines the natural excitation technique (NExT) and stochastic subspace identification (SSI) method is developed. The new method is compared with the traditional SSI method, and mode frequency analysis is made based on a series of field tests carried out at the subway and power tunnel. It is found that both stability and efficiency of the mode frequency identification have been greatly improved, and it more suitable for online monitoring. Meanwhile, a mathematical model is used to analyze the original mode characteristics and the influence of soil coupling. The results are also compared with the field tests results by using the NExT-SSI method, and some recommendations are also made for how to choose the vibration modals for vibration-based monitoring in the tunnel.

scholarly journals Region-Based CNN Method with Deformable Modules for Visually Classifying Concrete Cracks

2020 ◽  
Vol 10 (7) ◽  
pp. 2528 ◽  
Author(s):  
Lu Deng ◽  
Hong-Hu Chu ◽  
Peng Shi ◽  
Wei Wang ◽  
Xuan Kong
Keyword(s):  
Crack Detection ◽  
Detection Methods ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Out Of Plane ◽  
Intelligent Detection ◽  
New Type ◽  
Feature Pyramid ◽  
Crack Detector ◽  
Core Idea

Cracks are often the most intuitive indicators for assessing the condition of in-service structures. Intelligent detection methods based on regular convolutional neural networks (CNNs) have been widely applied to the field of crack detection in recently years; however, these methods exhibit unsatisfying performance on the detection of out-of-plane cracks. To overcome this drawback, a new type of region-based CNN (R-CNN) crack detector with deformable modules is proposed in the present study. The core idea of the method is to replace the traditional regular convolution and pooling operation with a deformable convolution operation and a deformable pooling operation. The idea is implemented on three different regular detectors, namely the Faster R-CNN, region-based fully convolutional networks (R-FCN), and feature pyramid network (FPN)-based Faster R-CNN. To examine the advantages of the proposed method, the results obtained from the proposed detector and corresponding regular detectors are compared. The results show that the addition of deformable modules improves the mean average precisions (mAPs) achieved by the Faster R-CNN, R-FCN, and FPN-based Faster R-CNN for crack detection. More importantly, adding deformable modules enables these detectors to detect the out-of-plane cracks that are difficult for regular detectors to detect.

scholarly journals Deep Learning Method on Target Echo Signal Recognition for Obscurant Penetrating Lidar Detection in Degraded Visual Environments

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3424
Author(s):  
Xujia Liang ◽  
Zhonghua Huang ◽  
Liping Lu ◽  
Zhigang Tao ◽  
Bing Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Mobile Robotics ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Detection Methods ◽  
Signal Recognition ◽  
Echo Signal ◽  
Deep Learning Algorithm ◽  
Recognition Criterion

With the rapid development of autonomous vehicles and mobile robotics, the desire to advance robust light detection and ranging (Lidar) detection methods for real world applications is increasing. However, this task still suffers in degraded visual environments (DVE), including smoke, dust, fog, and rain, as the aerosols lead to false alarm and dysfunction. Therefore, a novel Lidar target echo signal recognition method, based on a multi-distance measurement and deep learning algorithm is presented in this paper; neither the backscatter suppression nor the denoise functions are required. The 2-D spectrogram images are constructed by using the frequency-distance relation derived from the 1-D echo signals of the Lidar sensor individual cell in the course of approaching target. The characteristics of the target echo signal and noise in the spectrogram images are analyzed and determined; thus, the target recognition criterion is established accordingly. A customized deep learning algorithm is subsequently developed to perform the recognition. The simulation and experimental results demonstrate that the proposed method can significantly improve the Lidar detection performance in DVE.

Nondestructive Damage Detection of Bridges: A Status Review

2001 ◽  
Vol 4 (2) ◽  
pp. 75-91 ◽  
Author(s):  
Xiaotong Wang ◽  
Chih-Chen Chang ◽  
Lichu Fan
Keyword(s):  
Damage Detection ◽  
Rapid Development ◽  
Low Frequency ◽  
Basic Research ◽  
Digital Signal ◽  
Point Of View ◽  
Detection Methods ◽  
Measured Signal ◽  
Destructive Testing ◽  
Bridge Damage

The recent advances in detecting and locating damage in bridges by different kinds of non-destructive testing and evaluation (NDT&E) methods are reviewed. From the application point of view, classifications for general bridge components and their damage types are presented. The relationships between damage, bridge components, and NDT&E techniques are summarized. Many useful WEB sources of NDT&E techniques in bridge damage detection are given. It is concluded that: (1) vibration-based damage detection methods are successful to a certain extent, especially when the overall damage is significant and, low frequency vibration can identify those areas where more detailed local inspection should be concentrated; (2) robust identification techniques that are able to locate damage based on realistic measured data sets still seem a long way from reality, and, basic research is still necessary in the mean time; (3) the rapid development of computer technology and digital signal processing (DSP) techniques greatly impacts upon the conventional NDT techniques, especially in control data processing and data displaying, as well as in simulation and modeling; (4) most of the NDT&E techniques introduced in this paper have their own practical commercial systems, but the effort required for combining the theoretical, experimental and engineering achievements, is still a challenging task when establishing the relationship between the unknown quantities and the measured signal parameters and specialised instruments have shown great advantages for doing some things more effectively than general ones; (5) in bridge damage detection, a problem usually requires the application of different NDT&E techniques; two or more independent techniques are needed to enable confidence in the results.

Laser-Ultrasound Testing of Cracks in Porcelain Insulator

2013 ◽  
Vol 774-776 ◽  
pp. 1349-1352
Author(s):  
Ru Feng Hou ◽  
Wen Hong Wang ◽  
Run Yang Mo ◽  
Xiao Jun Liu
Keyword(s):  
Cylindrical Surface ◽  
Laser Generation ◽  
Laser Ultrasound ◽  
Laser Ultrasonic ◽  
Porcelain Insulator ◽  
On Line ◽  
Porcelain Insulators ◽  
Ultrasound Testing

Detected the cracks in porcelain insulator is a strategy to prevent failure. This study proposes a remote detect technique to inspect the cracks in on-line porcelain insulator based on laser-generation based imaging (LGBI) method. Two porcelain insulators samples A and B were designed. Samples A is a 10kv porcelain insulator work outdoors, it has five artificial notch-type defects in the cylindrical surface of porcelain insulator. Sample B is a 10kv porcelain insulator work indoor with aging cracks. All defects in two specimens were detected by laser ultrasonic visualizing inspector (LUVI). Images are quite convenient to confirm cracks morphology. The experiment proves that the cracks in porcelain insulator can be detected by LUVI system.

scholarly journals Marine Toxins Detection by Biosensors Based on Aptamers

Toxins ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 1 ◽  
Author(s):  
Wei Ye ◽  
Taomei Liu ◽  
Weimin Zhang ◽  
Muzi Zhu ◽  
Zhaoming Liu ◽  
...  
Keyword(s):  
Detection Limit ◽  
Sensitivity And Specificity ◽  
High Efficiency ◽  
Rapid Development ◽  
High Sensitivity ◽  
Detection Methods ◽  
Marine Toxins ◽  
Time Saving ◽  
Low Detection Limit ◽  
The Future

Marine toxins cause great harm to human health through seafood, therefore, it is urgent to exploit new marine toxins detection methods with the merits of high sensitivity and specificity, low detection limit, convenience, and high efficiency. Aptasensors have emerged to replace classical detection methods for marine toxins detection. The rapid development of molecular biological approaches, sequencing technology, material science, electronics and chemical science boost the preparation and application of aptasensors. Taken together, the aptamer-based biosensors would be the best candidate for detection of the marine toxins with the merits of high sensitivity and specificity, convenience, time-saving, relatively low cost, extremely low detection limit, and high throughput, which have reduced the detection limit of marine toxins from nM to fM. This article reviews the detection of marine toxins by aptamer-based biosensors, as well as the selection approach for the systematic evolution of ligands by exponential enrichment (SELEX), the aptamer sequences. Moreover, the newest aptasensors and the future prospective are also discussed, which would provide thereotical basis for the future development of marine toxins detection by aptasensors.

Structural crack detection using deep learning–based fully convolutional networks

2019 ◽  
Vol 22 (16) ◽  
pp. 3412-3419 ◽  
Author(s):  
Xiao-Wei Ye ◽  
Tao Jin ◽  
Peng-Yu Chen
Keyword(s):  
Edge Detection ◽  
Structural Damage ◽  
Crack Detection ◽  
Training Data ◽  
Detection Methods ◽  
Crack Identification ◽  
Potential Threat ◽  
Online Data ◽  
Convolutional Network ◽  
Data Set

Cracks are a potential threat to the safety and endurance of civil infrastructures, and therefore, careful and regular structural crack inspection is needed during their long-term service periods. Many image-processing approaches have been developed for structural crack detection. However, like traditional edge detection algorithms, these methods are easily disturbed by the environmental effect. Convolutional neural networks are newly developed methods and have excellent performances in the image-classification tasks. This study proposes a fully convolutional network called Ci-Net for structural crack identification. Pixel-level labeled image training data are obtained from the online data set. Four indices are adopted to evaluate the performance of the trained Ci-Net. Crack images from an indoor concrete beam test are adopted for validation of its structural crack recognition capacity. The recognition results are also compared with those obtained by the edge detection methods. It indicates that Ci-Net exhibits a better performance over the edge detection methods in structural damage detection.

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