Semi-Automatic Defects Characterization by Phased Array Ultrasonic Testing : application to Welds

L’automatisation de détection des défauts dans les matériaux de structure et leur caractérisation dans le domaine du contrôle non destructif existe depuis plusieurs années et implique différentes techniques telle que la radiographie et les ultrasons. Plus particulièrement, l’utilisation des ultrasons multiéléments (PAUT) a connu durant ces dernières années un important développement notamment dans le contrôle des soudures, ce qui lui a valu d’être toujours un sujet d’actualité en liaison avec l’automatisation de détection basée sur les images PAUT. Ce travail de thèse recense tout d’abord les approches de détection automatiques développées dans la littérature en lien avec la présente thèse. Pour des raisons d’efficacité, nous n’avons pas retenu celles basées sur l’apprentissage statistique car elles requièrent des données volumineuses et des temps de traitement plus longs. Dans ce travail de thèse, nous proposons l’application d’un nouveau critère permettant de détecter des anomalies à partir d’une suite d’images PAUT effectués lors de différents contrôles de soudures. Nous avons ainsi étudié la performance de la méthodologie proposée en termes de détection et de dimensionnement de différents défauts (longueur et hauteur). Cela a été effectué à l’aide d’une défauthèque inédite, composée de plusieurs cas de défauts de natures diverses et variées, qui a été construite dans le cadre de cette thèse. Nous montrons ainsi que le code développé dans le cadre de cette thèse répond bien au besoin industriel fixé initialement pour ce qui est de la détection des différents défauts et l’estimation de leur longueur. En particulier, ledit code de calcul ne requiert pas beaucoup de mémoire et fournit des résultats comparables à ceux trouvés par des contrôleurs certifiés en un temps raisonnablement court tout en évitant les erreurs liées aux facteurs humains que nous rencontrons habituellement dans différentes méthodes de contrôle non destructif. Ainsi, la performance de l’approche mise en place est discutée en fonction des paramètres mis en jeu tels que le type de filtre et la technique de construction de l’image de référence. La complexité de la mesure de la hauteur des défauts et son adéquation avec les problématiques industrielles contemporaines sont également discutées notamment dans le cadre de la détection d’une et/ou plusieurs indications. Enfin, nous présentons une seconde application et ce dans le but d’effectuer une discrimination entre les micro-endommagements causés par l’attaque par hydrogène à haute température (HTHA) et les inclusions dans les aciers carbone. Deux méthodologies ont été proposées à cet effet. La première est effectuée à l’aide de l’apprentissage statistique alors que la seconde se base sur le système expert. Un nouveau critère a été alors développé en concertation avec des experts dans ce domaine. Les résultats ont montré la fiabilité qui devrait également être étendue pour inclure de nouvelles bases de données.The automation of defects detection in structural materials and their characterization in the field of nondestructive testing has existed for several years and involves different techniques such as radiography and ultrasound. In particular, the use of phased array ultrasonic testing (PAUT) has experienced in recent years a significant development in particular in the control of welds, which has made it a topic of current interest in connection with the automation of detection based on PAUT images. This thesis first lists the automatic detection approaches developed in the literature in connection with the present thesis. For efficiency reasons, we have not retained those based on statistical learning because they require large data and longer processing times. In this thesis, we propose the application of a new criterion to detect anomalies from a series of PAUT images taken during different weld inspections. We have studied the performance of the proposed methodology in terms of detection and sizing of different defects (length and height). This was done using a new defect database, composed of several cases of defects of various natures, which was built in the framework of this thesis. We show that the code developed in the framework of this thesis meets the industrial need initially set for the detection of various defects and the estimation of their length. In particular, the processing code does not require a lot of memory and provides results comparable to those found by certified inspectors in a reasonably short time while avoiding the errors related to human factors that we usually encounter in various non-destructive testing methods. Thus, the performance of the implemented approach is discussed depending on the parameters involved, such as the type of filter and the reference image construction technique. The complexity of the measurement of the height of defects and its adequacy with contemporary industrial problems are also discussed, particularly in the context of the detection of one or more indications. Finally, we present a second application with the aim of discriminating between micro-damage caused by high temperature hydrogen attack (HTHA) and inclusions in carbon steels. Two methodologies have been proposed for this purpose. The first one is performed using statistical learning while the second one is based on the expert system. A new criterion was then developed in consultation with experts in this field. The results showed its reliability, which should also be extended to include new databases

Development of an assembly for the realization of a transducer able to operate at very high temperatures

Monitoring the operation of the latest-generation nuclear reactor requires ultrasonic transducers able to operate at very high temperatures (> 600°C). To achieve this, CEA has requested from “Institut de Soudure” to help developing a new technology for these transducers compared to the one previously developed. This began with the development of a reliable assembly technique between a lithium niobate piezoelectric disc whose Curie temperature exceeds 1100°C and stainless steel discs. The chosen solution was to braze the niobate disc between two stainless steel discs. Parallel to this development, it was also necessary to develop a NDE procedure to verify the quality of the brazing assemblies. This development began with a simulation of immersion ultrasonic testing of the assemblies. The constraints were to be able to control the two brazed interfaces from the same access face, with the possibility of detecting and dimensioning defects with an equivalent diameter of 0.25 mm. This phase is important to define the optimal transducer with the associated operating conditions. The first assemblies validated the preliminary choices. To exploit the cartographies obtained, a signal processing procedure was developed. This enabled an automatic characterization of the indications observed. However, the analysis of the signals observed proved to be more complex than the one predicted by the simulation. Once the origin of the various observed signals was identified it was then possible to define windows allowing the construction of the cartographies to analyze. In case of a good quality assembly, it was possible to qualify the generated beam and to image it in the focal plane but with an observed signal having a very low damping. These first encouraging results, however, show that there is still some validation and development work to increase the sensitivity of the developed translator and its damping

A Semi-Supervised Based K-Means Algorithm for Optimal Guided Waves Structural Health Monitoring: A Case Study

This paper concerns the health monitoring of pipelines and tubes. It proposes the k-means clustering algorithm as a simple tool to monitor the integrity of a structure (i.e., detecting defects and assessing their growth). The k-means algorithm is applied on data collected experimentally, by means of an ultrasonic guided waves technique, from healthy and damaged tubes. Damage was created by attaching magnets to a tube. The number of magnets was increased progressively to simulate an increase in the size of the defect and also, a change in its shape. To test the performance of the proposed method for damage detection, a statistical population was created for the healthy state and for each damage step. This was done by adding white Gaussian noise to each acquired signal. To optimize the number of clusters, many algorithms were run, and their results were compared. Then, a semi-supervised based method was proposed to determine an alarm threshold, triggered when a defect becomes critical