Automatic defect detection and depth estimation using pulsed thermography

L’évaluation non-destructive (END) est une branche de la science qui s’intéresse à l’uniformité, la qualité et la conformité des matériaux et les composants qu’ils servent à construire. Les techniques de END visent à repérer et à mesurer les caractéristiques principales des matériaux sans en affecter ou à en détruire la structure ou la fonctionnalité. L’END permet d’observer les propriétés internes des pièces et de détecter les défauts sous leur surface. Cette approche est devenue graduellement une technologie importante pour garantir la sécurité et la fiabilité de plusieurs composantes de système en design, en fabrication et en développement de produits. La thermographie infrarouge est une approche d’END sans contact rapide qui utilise des caméras thermiques. Elle permet de détecter l’énergie thermique émise par les objets et à en afficher la distribution en température de la surface du spécimen sous observation. Dans ce projet, notre objectif est d’exploiter la thermographie infrarouge pour détecter les défauts sous la surface des objets. Plus spécialement, nous nous intéressons à la localisation des défauts et à l’estimation de leur profondeur sous la surface. Le manuscrit présente une investigation de différentes méthodes de localisation de défauts et de mesure de leur profondeur des défauts sous la surface pour différentes catégories de matériaux.Non-Destructive Testing (NDT) is an aspect of science concerning on uniformity, quality and serviceability of materials and their components. NDT techniques attempt to inspect and measure significant features of materials without changing or destroying their structure or functionality. NDT makes it possible to observe the internal properties of parts and detect the undersurface defects. NDT has progressively become an important technology to assure safety and reliability of many system components in the design, manufacturing and development areas. Infrared thermography is essentially a fast non-contact NDT inspection method that uses thermographic cameras. This technique detects the infrared energy emitted from objects and displays the corresponding temperature distributions on the specimen. In this project, we aim to use infrared thermography for detecting subsurface defects. Localizing the defects and estimating their depths are the important problems to be addressed in our research project. The manuscript investigates different methods related to these challenges

A novel application of image processing for the detection of rail surface RCF damage and incorporation in a crack growth model

The paper presents the development of an intelligent image processing algorithm capable of detecting fatigue defects from images of the rail surface. The links between the defect detection algorithm and 3D models for rail crack propagation are investigated, considering the influence of input parameters (materials, vehicle characteristics, loading conditions). The dynamic behaviour at the wheel-rail interface resulting in contact forces responsible for stressing and straining the rail material are imported from vehicle dynamics simulations. The integration of the simulated results from vehicle dynamics, contact and fracture mechanics models offer more reliable estimation of the stress intensity factors (SIF). Also the sensitivity analysis related to materials, vehicle characteristics, and loading conditions will provide further understanding of the factors that influence crack propagation in rails such as shear stresses, hydraulic pressure, fluid entrapment and squeeze film effect. This novel application of image processing for the detection of rail surface rolling contact fatigue (RCF) damage and automatic incorporation in a crack growth model represents an important contribution to the development of modern techniques for non-destructive rail inspection. This will result in improved planning/scheduling of future rail maintenance (e.g. rail grinding, renewal), less disruptions and reduced track maintenance costs in rail industry

Program planning, chapter 5, part D

Recommendations for future activities necessary to support satellite microwave sensing are reported. Areas covered include component development, data processing, calibration, design and fabrication of multifrequency systems, and experimental test programs to establish interactions of electromagnetic waves and sensed parameters

Two Dimensional Clipping Based Segmentation Algorithm for Grayscale Fingerprint Images

One of the huge methods in Automated Fingerprint Identification System (AFIS) is the segment or separation of the fingerprint. The process of decomposing an image into exclusive components is referred as segmentation. Fingerprint segmentation is the one of the predominant process involved in fingerprint pre-processing and it refers to the method of dividing or separating the image into disjoint areas as the foreground and the background region. The foreground also called as Region of Interest (ROI) due to the fact only the region which contains ridge and valley structure is used for processing, whilst the background carries noisy and irrelevant content material and so that it will be discarded in later enhancement or orientation or classification method. The challenge proper right here is to decide which a part of the image belongs to the foreground, retrieved as an input from the fingerprint sensor device or from benchmark datasets and which part belongs to the background. A 100% correct segmentation is continually very tough, specifically inside the very poor quality image or partial image together with the presence of latent. In this paper, we discuss a modified clipped based segmentation algorithm by adopting threshold value and canny edge detection techniques. We segment the background image is x and y dimensions or in other words left the edge, right edge, top edge and bottom edge of the image. For the purpose of analyzing the algorithm FVC ongoing 2002 benchmark dataset is considered. The entire algorithm is implemented using MATLAB 2015a. The algorithm is able to find affectively ROI of the fingerprint image or separates the foreground region from the background area of the fingerprint image very effectively. In high configuration system proposed algorithm achieves execution time of 1.75 seconds