Sparse Low-Rank Tensor Decomposition for Metal Defect Detection Using Thermographic Imaging Diagnostics

With the increasing use of induction thermography (IT) for non-destructive testing (NDT) in the mechanical and rail industry, it becomes necessary for the manufactures to rapidly and accurately monitor the health of specimens. The most general problem for IT detection is due to strong noise interference. In order to counter it, general post-processing is carried out. However, due to the more complex nature of noise and irregular shape specimens, this task becomes difficult and challenging. In this paper, a low-rank tensor with a sparse mixture of Gaussian (MoG) (LRTSMoG) decomposition algorithm for natural crack detection is proposed. The proposed algorithm models jointly the low rank tensor and sparse pattern by using a tensor decomposition framework. In particular, the weak natural crack information can be extracted from strong noise. Low-rank tensor based iterative sparse MoG noise modeling is carried out to enhance the weak natural crack information as well as reducing the computational cost. In order to show the robustness and efficacy of the model, experiments are conducted for natural crack detection on a variety of specimens. A comparative analysis is presented with general tensor decomposition algorithms. The algorithms are evaluated quantitatively based on signal-to-noise-ratio (SNR) along with the visual comparative analysis

Compressed Sensing for Open-ended Waveguide Non-Destructive Testing and Evaluation

Ph. D. ThesisNon-destructive testing and evaluation (NDT&E) systems using open-ended waveguide (OEW) suffer from critical challenges. In the sensing stage, data acquisition is time-consuming by raster scan, which is difficult for on-line detection. Sensing stage also disregards demand for the latter feature extraction process, leading to an excessive amount of data and processing overhead for feature extraction. In the feature extraction stage, efficient and robust defect region segmentation in the obtained image is challenging for a complex image background. Compressed sensing (CS) demonstrates impressive data compression ability in various applications using sparse models. How to develop CS models in OEW NDT&E that jointly consider sensing & processing for fast data acquisition, data compression, efficient and robust feature extraction is remaining challenges. This thesis develops integrated sensing-processing CS models to address the drawbacks in OEW NDT systems and carries out their case studies in low-energy impact damage detection for carbon fibre reinforced plastics (CFRP) materials. The major contributions are: (1) For the challenge of fast data acquisition, an online CS model is developed to offer faster data acquisition and reduce data amount without any hardware modification. The images obtained with OEW are usually smooth which can be sparsely represented with discrete cosine transform (DCT) basis. Based on this information, a customised 0/1 Bernoulli matrix for CS measurement is designed for downsampling. The full data is reconstructed with orthogonal matching pursuit algorithm using the downsampling data, DCT basis, and the customised 0/1 Bernoulli matrix. It is hard to determine the sampling pixel numbers for sparse reconstruction when lacking training data, to address this issue, an accumulated sampling and recovery process is developed in this CS model. The defect region can be extracted with the proposed histogram threshold edge detection (HTED) algorithm after each recovery, which forms an online process. A case study in impact damage detection on CFRP materials is carried out for validation. The results show that the data acquisition time is reduced by one order of magnitude while maintaining equivalent image quality and defect region as raster scan. (2) For the challenge of efficient data compression that considers the later feature extraction, a feature-supervised CS data acquisition method is proposed and evaluated. It reserves interested features while reducing the data amount. The frequencies which reveal the feature only occupy a small part of the frequency band, this method finds these sparse frequency range firstly to supervise the later sampling process. Subsequently, based on joint sparsity of neighbour frame and the extracted frequency band, an aligned spatial-spectrum sampling scheme is proposed. The scheme only samples interested frequency range for required features by using a customised 0/1 Bernoulli measurement matrix. The interested spectral-spatial data are reconstructed jointly, which has much faster speed than frame-by-frame methods. The proposed feature-supervised CS data acquisition is implemented and compared with raster scan and the traditional CS reconstruction in impact damage detection on CFRP materials. The results show that the data amount is reduced greatly without compromising feature quality, and the gain in reconstruction speed is improved linearly with the number of measurements. (3) Based on the above CS-based data acquisition methods, CS models are developed to directly detect defect from CS data rather than using the reconstructed full spatial data. This method is robust to texture background and more time-efficient that HTED algorithm. Firstly, based on the histogram is invariant to down-sampling using the customised 0/1 Bernoulli measurement matrix, a qualitative method which only gives binary judgement of defect is developed. High probability of detection and accuracy is achieved compared to other methods. Secondly, a new greedy algorithm of sparse orthogonal matching pursuit (spOMP)-based defect region segmentation method is developed to quantitatively extract the defect region, because the conventional sparse reconstruction algorithms cannot properly use the sparse character of correlation between the measurement matrix and CS data. The proposed algorithms are faster and more robust to interference than other algorithms.China Scholarship Counci

Artificial vision by thermography : calving prediction and defect detection in carbon fiber reinforced polymer

La vision par ordinateur est un domaine qui consiste à extraire ou identifier une ou plusieurs informations à partir d’une ou plusieurs images dans le but soit d’automatiser une tache, soit de fournir une aide à la décision. Avec l’augmentation de la capacité de calcul des ordinateurs, la vulgarisation et la diversification des moyens d’imagerie tant dans la vie quotidienne, que dans le milieu industriel,ce domaine a subi une évolution rapide lors de dernières décennies. Parmi les différentes modalités d’imagerie pour lesquels il est possible d’utiliser la vision artificielle cette thèse se concentre sur l’imagerie infrarouge. Plus particulièrement sur l’imagerie infrarouge pour les longueurs d’ondes comprises dans les bandes moyennes et longues. Cette thèse se porte sur deux applications industrielles radicalement différentes. Dans la première partie de cette thèse, nous présentons une application de la vision artificielle pour la détection du moment de vêlage en milieux industriel pour des vaches Holstein. Plus précisément l’objectif de cette recherche est de déterminer le moment de vêlage en n’utilisant que des données comportementales de l’animal. À cette fin, nous avons acquis des données en continu sur différents animaux pendant plusieurs mois. Parmi les nombreux défis présentés par cette application l’un d’entre eux concerne l’acquisition des données. En effet, les caméras que nous avons utilisées sont basées sur des capteurs bolométriques, lesquels sont sensibles à un grand nombre de variables. Ces variables peuvent être classées en quatre catégories : intrinsèque, environnemental, radiométrique et géométrique. Un autre défit important de cette recherche concerne le traitement des données. Outre le fait que les données acquises utilisent une dynamique plus élevée que les images naturelles ce qui complique le traitement des données ; l’identification de schéma récurrent dans les images et la reconnaissance automatique de ces derniers grâce à l’apprentissage automatique représente aussi un défi majeur. Nous avons proposé une solution à ce problème. Dans le reste de cette thèse nous nous sommes penchés sur la problématique de la détection de défaut dans les matériaux, en utilisant la technique de la thermographie pulsée. La thermographie pulsée est une méthode très populaire grâce à sa simplicité, la possibilité d’être utilisée avec un grand nombre de matériaux, ainsi que son faible coût. Néanmoins, cette méthode est connue pour produire des données bruitées. La cause principale de cette réputation vient des diverses sources de distorsion auquel les cameras thermiques sont sensibles. Dans cette thèse, nous avons choisi d’explorer deux axes. Le premier concerne l’amélioration des méthodes de traitement de données existantes. Dans le second axe, nous proposons plusieurs méthodes pour améliorer la détection de défauts. Chaque méthode est comparée à plusieurs méthodes constituant l’état de l’art du domaine.Abstract Computer vision is a field which consists in extracting or identifying one or more information from one or more images in order either to automate a task or to provide decision support. With the increase in the computing capacity of computers, the popularization and diversification of imaging means, both in industry, as well as in everyone’s life, this field has undergone a rapid development in recent decades. Among the different imaging modalities for which it is possible to use artificial vision, this thesis focuses on infrared imaging. More particularly on infrared imagery for wavelengths included in the medium and long bands. This thesis focuses on two radically different industrial applications. In the first part of this thesis, we present an application of artificial vision for the detection of the calving moment in industrial environments for Holstein cows. More precisely, the objective of this research is to determine the time of calving using only physiological data from the animal. To this end, we continuously acquired data on different animals over several days. Among the many challenges presented by this application, one of them concerns data acquisition. Indeed, the cameras we used are based on bolometric sensors, which are sensitive to a large number of variables. These variables can be classified into four categories: intrinsic, environmental, radiometric and geometric. Another important challenge in this research concerns the processing of data. Besides the fact that the acquired data uses a higher dynamic range than the natural images which complicates the processing of the data; Identifying recurring patterns in images and automatically recognizing them through machine learning is a major challenge. We have proposed a solution to this problem. In the rest of this thesis we have focused on the problem of defect detection in materials, using the technique of pulsed thermography. Pulse thermography is a very popular method due toits simplicity, the possibility of being used with a large number of materials, as well as its low cost. However, this method is known to produce noisy data. The main cause of this reputation comes from the various sources of distortion to which thermal cameras are sensitive. In this thesis, we have chosen to explore two axes. The first concerns the improvement of existing data processing methods. In the second axis, we propose several methods to improve fault detection. Each method is compared to several methods constituting the state of the art in the field

Автоматизированная система тепловой дефектометрии многослойных материалов на основе глубокого обучения

Currently, along with growth in industrial production, the requirements for product quality testing are also increasing. In the tasks of defectoscopy and defectometry of multilayer materials, the use of thermal nondestructive testing method is promising. At the same time, interpretation of thermal testing data is complicated by a number of factors, which makes the use of traditional methods of data processing ineffective. Therefore, an urgent task is to search for new methods of thermal testing that will automate the diagnostic process and increase information content of obtained results. The purpose of article is to use the advances in deep learning for processing results of active thermal testing of products made of multilayer materials and development of an automated system for thermal defectoscopy and defectometry of such products. The proposed system consists of a heating source, an infrared camera for recording sequences of thermograms and a digital information processing unit. Three neural network modules are used for automated data processing, each of which performs one of the tasks: defects detection and classification, determination of the defect depth and thickness. The software algorithms and user interface for interacting with system are programmed in the NI LabVIEW development environment.Experimental studies on samples made of multilayer fiberglass have shown a significant advantage of the developed system over using traditional methods for analyzing thermal testing data. The defect classification (determining the type) error on the test dataset was 15.7 %. Developed system ensured determination of defect depth with a relative error of 3.2 %, as well as the defect thickness with a relative error of 3.5 %.На сегодняшний день, вместе с ростом темпов промышленного производства повышаются также и требования к контролю качества продукции. В задачах дефектоскопии и дефектометрии многослойных материалов перспективным является использование теплового метода неразрушающего контроля. В то же время, интерпретация данных теплового контроля усложнена рядом факторов, что делает использование традиционных методов анализа данных неэффективным. Поэтому актуальным заданием является поиск новых методов теплового контроля, которые позволят автоматизировать процесс диагностики и повысить информативность полученных результатов. Целью статьи являлось использование достижений в области глубокого обучения для обработки результатов активного теплового контроля изделий из многослойных материалов и разработка автоматизированной системы тепловой дефектоскопии и дефектометрии таких изделий.Предлагаемая система состоит из источника нагрева, тепловизора для регистрации последовательностей термограмм и блока цифровой обработки информации. Для автоматизированной обработки данных используются три нейросетевых модуля, каждый из которых выполняет одну из задач: обнаружение и классификация дефектов, определение глубины залегания дефекта и его раскрытия (толщины). Программные алгоритмы и интерфейс взаимодействия с системой выполнены в среде разработки NI LabVIEW.Экспериментальные исследования на образцах из многослойного стеклотекстолита показали значительное преимущество разработанной системы над методами, использующими традиционные алгоритмы анализа данных теплового контроля. Ошибка определения типа (классификации) дефекта на тестовом образце составила 15,7 %. Разработанная система обеспечила определение глубины дефекта с относительной погрешностью 3,2 %, а также толщины дефекта с относительной погрешностью 3,5 %

DeftectNet: Joint loss structured deep adversarial network for thermography defect detecting system

In this paper, a novel joint loss Generative Adversarial Networks (GAN) framework is proposed for thermography nondestructive testing named Defect-Detection Network (DeftectNet). A new joint loss function that incorporates both the modified GAN loss and penalty loss is proposed. The strategy enables the training process to be more stable and to significantly improve the detection rate. The obtained result shows that the proposed joint loss can better capture the salient features in order to improve the detection accuracy. In order to verify the effectiveness and robustness of the proposed method, experimental studies have been carried out for inner debond defects on both regular and irregular shaped carbon fiber reinforced polymer/plastic (CFRP) specimens. A comparison experiment has been undertaken to study the proposed method with other current state-of-the-art deep semantic segmentation algorithms. The promising results have been obtained where the performance of the proposed method can achieve end-to-end detection of defects

Deep Learning Automated System for Thermal Defectometry of Multilayer Materials

Currently, along with growth in industrial production, the requirements for product quality testing are also increasing. In the tasks of defectoscopy and defectometry of multilayer materials, the use of thermal non-destructive testing method is promising. At the same time, interpretation of thermal testing data is complicated by a number of factors, which makes the use of traditional methods of data processing ineffective. Therefore, an urgent task is to search for new methods of thermal testing that will automate the diagnostic process and increase information content of obtained results. The purpose of article is to use the advances in deep learning for processing results of active thermal testing of products made of multilayer materials and development of an automated system for thermal defectoscopy and defectometry of such products.The proposed system consists of a heating source, an infrared camera for recording sequences of thermograms and a digital information processing unit. Three neural network modules are used for automated data processing, each of which performs one of the tasks: defects detection and classification, determination of the defect depth and thickness. The software algorithms and user interface for interacting with system are programmed in the NI LabVIEW development environment.Experimental studies on samples made of multilayer fiberglass have shown a significant advantage of the developed system over using traditional methods for analyzing thermal testing data. The defect classification (determining the type) error on the test dataset was 15.7％. Developed system ensured determination of defect depth with a relative error of 3.2％, as well as the defect thickness with a relative error of 3.5％.На сегодняшний день, вместе с ростом темпов промышленного производства повышаются также и требования к контролю качества продукции. В задачах дефектоскопии и дефектометрии многослойных материалов перспективным является использование теплового метода неразрушающего контроля. В то же время, интерпретация данных теплового контроля усложнена рядом факторов, что делает использование традиционных методов анализа данных неэффективным. Поэтому актуальным заданием является поиск новых методов теплового контроля, которые позволят автоматизировать процесс диагностики и повысить информативность полученных результатов. Целью статьи являлось использование достижений в области глубокого обучения для обработки результатов активного теплового контроля изделий из многослойных материалов и разработка автоматизированной системы тепловой дефектоскопии и дефектометрии таких изделий. Предлагаемая система состоит из источника нагрева, тепловизора для регистрации последовательностей термограмм и блока цифровой обработки информации. Для автоматизированной обработки данных используются три нейросетевых модуля, каждый из которых выполняет одну из задач: обнаружение и классификация дефектов, определение глубины залегания дефекта и его раскрытия (толщины). Программные алгоритмы и интерфейс взаимодействия с системой выполнены в среде разработки NI LabVIEW. Экспериментальные исследования на образцах из многослойного стеклотекстолита показали значительное преимущество разработанной системы над методами, использующими традиционные алгоритмы анализа данных теплового контроля. Ошибка определения типа (классификации) дефекта на тестовом образце составила 15,7 ％. Разработанная система обеспечила определение глубины дефекта с относительной погрешностью 3,2 ％, а также толщины дефекта с относительной погрешностью 3,5 ％

Тестування нейромережевих модулів системи теплової дефектометрії за допомогою імітаційного моделювання

На основі аналізу існуючих видів та методів активного теплового контролю розроблено автоматизовану систему аналізу даних теплового контролю для визначення характеристик дефектів у багатошарових матеріалах. Основними складниками програмної частини системи є три нейромережеві модулі, кожен з яких призначений для вирішення окремої задачі – визначення типу, глибини залягання і товщини дефекту.Based on the analysis of existing types and methods of active thermal non-destructive testing, an automated system of data analysis of thermal testing has been developed to determine the characteristics of defects in multilayer materials. The main components of the software part of the system are three neural network modules. The modular structure facilitates the construction and modification of the system, increases the overall efficiency of its work by optimizing the settings of the modules to solve specific problems

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing