Detection and Estimation of Defect Depth in Infrared Thermography Using Artificial Neural Networks and Fuzzy Logic PDF Download
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Languages : en
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Author: Qiang Fang
Publisher:
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Category :
Languages : en
Pages : 227
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Non-destructive evaluation (NDE) is a field to identify all types of structural damage in an object of interest without applying any permanent damage and modification. This field has been intensively investigated for many years. The infrared thermography (IR) is one of NDE technology through inspecting, characterize and analyzing defects based on the infrared images (sequences) from the recordation of infrared light emission and reflection to evaluate non-self-heating objects for quality control and safety assurance. In recent years, the deep learning field of artificial intelligence has made remarkable progress in image processing applications. This field has shown its ability to overcome most of the disadvantages in other approaches existing previously in a great number of applications. Whereas due to the insufficient training data, deep learning algorithms still remain unexplored, and only few publications involving the application of it for thermography nondestructive evaluation (TNDE). The intelligent and highly automated deep learning algorithms could be coupled with infrared thermography to identify the defect (damages) in composites, steel, etc. with high confidence and accuracy. Among the topics in the TNDE research field, the supervised and unsupervised machine learning techniques both are the most innovative and challenging tasks for defect detection analysis. In this project, we construct integrated frameworks for processing raw data from infrared thermography using deep learning algorithms and highlight of the methodologies proposed include the following: 1. Automatic defect identification and segmentation by deep learning algorithms in infrared thermography. The pre-trained convolutional neural networks (CNNs) are introduced to capture defect feature in infrared thermal images to implement CNNs based models for the detection of structural defects in samples made of composite materials (fault diagnosis). Several alternatives of deep CNNs for the detection of defects in the Infrared thermography. The comparisons of performance of the automatic defect detection and segmentation in infrared thermography using different deep learning detection methods: (i) instance segmentation (Center-mask; Mask-RCNN); (ii) objective location (Yolo-v3; Faster-RCNN); (iii) semantic segmentation (Unet; Res-unet); 2. Data augmentation technique through synthetic data generation to reduce the cost of high expense associated with the collection of original infrared data in the composites (aircraft components.) to enrich training data for feature learning in TNDE; 3. The generative adversarial network (Deep convolutional GAN and Wasserstein GAN) is introduced to the infrared thermography associated with partial least square thermography (PLST) (PLS-GANs network) for visible feature extraction of defects and enhancement of the visibility of defects to remove noise in Pulsed thermography; 4. Automatic defect depth estimation (Characterization issue) from simulated infrared data using a simplified recurrent neural network: Gate Recurrent Unit (GRU) through the regression supervised learning.
Author: Xavier P.V. Maldague
Publisher: Springer Science & Business Media
ISBN: 1447119959
Category : Technology & Engineering
Languages : en
Pages : 231
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With national trade barriers falling, causing the expansion of the com petitive global market, the question of quality control has become an essential issue for the 1990s. The time where the promise was to replace a product if it does not work seems to have passed; what is more impor tant now is not so much a reduction in what is going wrong but an increase of what is going right the first time (Feigenbaum 1990). This new trend is sometimes referred to as total quality. Among the many advantages ofthis zero-defect manufacturing policy, we can enumerate (Laurin 1990): superior marketability of wholly de pendable products, enormous gain in productivity, elimination of waste ful cost in replacing poor quality work and retrofitting rejected products from the field. Although total quality is a relatively new and attractive concept for mass products such as cars, consumer electronics and per sonal computers, in many fields, mainly aerospace and military, it has been the rule for years because of security reasons.
Author: Chun Yin
Publisher: Springer Nature
ISBN: 9819982162
Category : Technology & Engineering
Languages : en
Pages : 280
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The book focuses on infrared thermographic NDT systems and approaches. Both principles and engineering practice are covered, with more emphasis on the engineering practice of spacecraft damage detection and analysis. This is achieved by providing an in-depth study of several major topics such as infrared feature extraction, damage reconstruction, reconstructed image fusion, reconstructed image stitching, reconstructed image segmentation, defect positioning, defect edge detection and quantitative calculation. A number of application cases are discussed in detail, including impact damage to single-layer and multi-layer protective configurations, simple impact damage, and complex multi-type impact damage. The comprehensive and systematic treatment of practical problems in infrared detection and spacecraft damage identification is one of the main features of this book, which is particularly suitable for those interested in learning practical solutions in infrared detection technology. This book can benefit researchers, engineers, and graduate students in the fields of aerospace design and manufacturing, spacecraft environmental engineering, and non-destructive testing technology, etc.
Author: Peyman Hedayati Vahid
Publisher:
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Category :
Languages : en
Pages :
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Author: Vladimir Vavilov
Publisher: Springer Nature
ISBN: 303048002X
Category : Technology & Engineering
Languages : en
Pages : 610
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This is the first book summarizing the theoretical basics of thermal nondestructive testing (TNDT) by combining elements of heat conduction, infrared thermography, and industrial nondestructive testing. The text contains the physical models of TNDT, heat transfer in defective and sound structures, and thermal properties of materials. Also included are the optimization of TNDT procedures, defect characterization, data processing in TNDT, active and passive TNDT systems, as well as elements of statistical data treatment and decision making. This text contains in-depth descriptions of applications in infrared/thermal testing within aerospace, power production, building, as well as the conservation of artistic monuments The book is intended for the industrial specialists who are involved in technical diagnostics and nondestructive testing. It may also be useful for academic researchers, undergraduate, graduate and PhD university students.
Author:
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Category : Thermography
Languages : en
Pages : 548
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Author: Mohammad Hossein Ahmadi
Publisher:
ISBN:
Category :
Languages : en
Pages : 35
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Pulse Phase Thermography (PPT) has been introduced as a novel robust Non-Destructive Testing (NDT) Infrared Thermography (IRT) technique. It employs Discrete Fourier Transform (DFT) to thermal images obtained following flash heating of the front surface of a specimen to extract the phase delay (or phase) information. The computed phase grams (or phase maps) are used for defect visualization in many materials. The temperature contrast enables defect detection based on thermographic data. However, thermal images usually involve significant measurement noise and non-uniform backgrounds caused by uneven heating and environmental reflections. As a result, it is not easy to recognize the defective regions efficiently. In this work, we applied Long Short-Term Memory (LSTM) and Convolutions Neural Networks works (CNNs) based on deep learning (DL) models to defect detection and defect depth classification from thermographic image data. Our experimental results showed that the proposed DL-based architecture achieved 0.95 and 0.77 accuracy scores for sound and defected pixels classification. Furthermore, the experimental results illustrated that LSTM and CNN techniques achieved 0.91 and 0.82 accuracies for defect-depth classification, respectively. Consequently, the LSTM technique overcame the CNNs technique for defect detection and defect-depth classification cases.
Author: Waldemar Minkina
Publisher: John Wiley & Sons
ISBN: 0470747188
Category : Technology & Engineering
Languages : en
Pages : 220
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In Infrared Thermography, the authors discuss the sources of uncertainty, including how to quantify these sources, associated with the use of thermal imagers. This book explains the common misunderstandings in the interpretation of temperature measurements, and provides a metrological evaluation of commercially available infrared cameras. It suggests how to best estimate the accuracy of thermal imaging instruments, whilst considering the level of accuracy attributed to measurements from these thermal imagers. Key features: Begins with an introduction to uncertainties and radiance terms before moving onto the issues surrounding thermal imaging. Deals with the basic issues of thermal imager measurements such as the law of heat exchange by radiation and emissivity. Describes a typical processing algorithm of the measurement path for an example infrared camera. Discusses measurement error analysis of a thermal imaging system. Considers the results of simulation research of thermography uncertainty. Includes an accompanying website which hosts MATLAB® code. Infrared Thermography is primarily aimed at quantitative thermographers, and manufacturers, vendors and users of thermal imagers. This book is also of interest to senior undergraduate and postgraduate students across a range of disciplines such as electrical, mechanical and civil engineering, computer science, and biomedicine.
Author:
Publisher:
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Category : Nondestructive testing
Languages : en
Pages : 1012
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