scholarly journals Nonlinear Ultrasound Crack Detection with Multi-Frequency Excitation—A Comparison

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5368
Author(s):  
Frank Mevissen ◽  
Michele Meo
Keyword(s):  
Crack Detection ◽  
Damage Identification ◽  
Turbine Blades ◽  
Detection Methods ◽  
Nonlinearity Parameter ◽  
Destructive Testing ◽  
Frequency Wave ◽  
Nonlinear Ultrasound ◽  
Frequency Excitation ◽  
Excitation Frequencies

Nonlinear ultrasound crack detection methods are used as modern, non-destructive testing tools for inspecting early damages in various materials. Nonlinear ultrasonic wave modulation, where typically two or more frequencies are excited, was demonstrated to be a robust method for failure indicators when using measured harmonics and modulated response frequencies. The aim of this study is to address the capability of multi-frequency wave excitation, where more than two excitation frequencies are used, for better damage identification when compared to single and double excitation frequencies without the calculation of dispersion curves. The excitation frequencies were chosen in such a way that harmonic and modulated response frequencies meet at a specific frequency to amplify signal energy. A new concept of nonlinearity parameter grouping with multi-frequency excitation was developed as an early failure parameter. An analytical solution of the one-dimensional wave equation was derived with four fundamental frequencies, and a total of 64 individual and 30 group nonlinearity parameters. Experimental validation of the approach was conducted on metal plates with different types of cracks and on turbine blades where cracks originated under service conditions. The results showed that the use of multi-frequency excitation offers advantages in detecting cracks.

Void and crack detection of polymethacrylimide foams based on terahertz time-domain spectroscopic imaging

2016 ◽  
Vol 19 (3) ◽  
pp. 348-363 ◽  
Author(s):  
Liyun Xing ◽  
Hong-Liang Cui ◽  
Changcheng Shi ◽  
Tianying Chang ◽  
Dongshan Wei ◽  
...  
Keyword(s):  
Time Domain ◽  
Crack Detection ◽  
Spectral Characteristics ◽  
Detection Methods ◽  
Terahertz Time Domain Spectroscopy ◽  
Destructive Testing ◽  
Imaging Results ◽  
Clear Identification ◽  
Reflection Imaging ◽  
The Time Domain

Terahertz reflection imaging is considered as a potential diagnostic tool for the investigation of polymethacrylimide foam defects. Using terahertz time-domain spectroscopy (TDS) and detection methods based on terahertz spectroscopic analysis, the reflection imaging results of different thicknesses of polymethacrylimide foam with two kinds of detections (cracks and voids) are studied. The samples (Degussa Rohacell WF71) are planar slabs of polymethacrylimide foams with thicknesses of 35 mm, 60.5 mm and 10 mm. It is found that the same kinds of polymethacrylimide foam defects with different foam thicknesses have similar spectral characteristics, with marked differences only in the amplitude and phase of the reflected wave. In view of this, we focus our study on the defective spectral characteristics of one of the foams (35 mm thickness). The characteristics of void and crack defects are analyzed in the time domain, which is based mainly on the variation of the reflected waveform. In particular, the imaging and clear identification of voids of less than 2.4 mm in diameter, fine cracks (0.3 mm wide), and the quantification of defects can be readily achieved using the terahertz non-destructive testing technique described here.

scholarly journals Pixel-Level Fatigue Crack Segmentation in Large-Scale Images of Steel Structures Using an Encoder–Decoder Network

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4135
Author(s):  
Chuanzhi Dong ◽  
Liangding Li ◽  
Jin Yan ◽  
Zhiming Zhang ◽  
Hong Pan ◽  
...  
Keyword(s):  
Fatigue Crack ◽  
Large Scale ◽  
Crack Detection ◽  
Fatigue Cracks ◽  
Steel Structures ◽  
Life Cycles ◽  
Detection Methods ◽  
Destructive Testing ◽  
Existing Structures ◽  
Fatigue Crack Detection

Fatigue cracks are critical types of damage in steel structures due to repeated loads and distortion effects. Fatigue crack growth may lead to further structural failure and even induce collapse. Efficient and timely fatigue crack detection and segmentation can support condition assessment, asset maintenance, and management of existing structures and prevent the early permit post and improve life cycles. In current research and engineering practices, visual inspection is the most widely implemented approach for fatigue crack inspection. However, the inspection accuracy of this method highly relies on the subjective judgment of the inspectors. Furthermore, it needs large amounts of cost, time, and labor force. Non-destructive testing methods can provide accurate detection results, but the cost is very high. To overcome the limitations of current fatigue crack detection methods, this study presents a pixel-level fatigue crack segmentation framework for large-scale images with complicated backgrounds taken from steel structures by using an encoder-decoder network, which is modified from the U-net structure. To effectively train and test the images with large resolutions such as 4928 × 3264 pixels or larger, the large images were cropped into small images for training and testing. The final segmentation results of the original images are obtained by assembling the segment results in the small images. Additionally, image post-processing including opening and closing operations were implemented to reduce the noises in the segmentation maps. The proposed method achieved an acceptable accuracy of automatic fatigue crack segmentation in terms of average intersection over union (mIOU). A comparative study with an FCN model that implements ResNet34 as backbone indicates that the proposed method using U-net could give better fatigue crack segmentation performance with fewer training epochs and simpler model structure. Furthermore, this study also provides helpful considerations and recommendations for researchers and practitioners in civil infrastructure engineering to apply image-based fatigue crack detection.

scholarly journals Pattern Deep Region Learning for Crack Detection in Thermography Diagnosis System

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 612 ◽  
Author(s):  
Jue Hu ◽  
Weiping Xu ◽  
Bin Gao ◽  
Gui Tian ◽  
Yizhe Wang ◽  
...  
Keyword(s):  
Crack Detection ◽  
Pattern Mining ◽  
Learning Method ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Pulsed Thermography ◽  
Testing Technology ◽  
Deep Region ◽  
Detection And Localization ◽  
Non Destructive

Eddy Current Pulsed Thermography is a crucial non-destructive testing technology which has a rapidly increasing range of applications for crack detection on metals. Although the unsupervised learning method has been widely adopted in thermal sequences processing, the research on supervised learning in crack detection remains unexplored. In this paper, we propose an end-to-end pattern, deep region learning structure to achieve precise crack detection and localization. The proposed structure integrates both time and spatial pattern mining for crack information with a deep region convolution neural network. Experiments on both artificial and natural cracks have shown attractive performance and verified the efficacy of the proposed structure.

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.

Advances in the Computed Tomography of Turbine Blades

2005 ◽  
Author(s):  
Martin Allen ◽  
Andrew T. Ramsey
Keyword(s):  
Computed Tomography ◽  
Data Extraction ◽  
Turbine Blades ◽  
Practical Method ◽  
International Standards ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Wide Range ◽  
Non Destructive ◽  
Testing And Evaluation

Recent advances in virtually all areas of industrial Computed Tomography (CT) now allow faster, higher resolution, and increasingly economic CT inspection of turbine blades than ever before. CT is now used for a wide range of Non Destructive Testing and Evaluation (NDT&E) applications including first article inspection, defect detection, internal measurement, wear (and failure) analysis, and reverse engineering. Improvements range from the introduction of international standards on CT, through improvements in acquisition, reconstruction, and data extraction. Some of the most significant advances have been made in the ability to process the data generated by the CT systems. Today, CT is an increasingly practical method for the Non Destructive Testing and Evaluation of turbine blades.

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.

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