Damage detection and localization in pipeline using sparse estimation of ultrasonic guided waves signals

International audienceThis paper focuses on the development of a method for damage detection and localization in pipeline structures. These structures are subject to variation of environmental and operational conditions (EOCs) which will have an impact on the collected signals. Since damage detection is generally based on comparison between the reference signals and the current signals acquired from the structure, the effects of EOCs will give rise to false alarm. This issue is addressed by selecting from the database of reference signals those with similar or very close EOCs. Such an operation can be performed by calculating the sparse estimation of the current signal. The estimation error is used as an indication of the presence of damage. Actually, a damage signal will be characterized by a high estimation error compared to that of a healthy signal. The position of the detected damage is obtained by calculating the estimation error on a sliding window over the damaged signal. This method was tested on signals collected on a small scale pipeline placed in laboratory conditions. Results have shown that the created damage was successfully detected and localized

Structural health monitoring using statistical learning methods : Application on tubular structures

Les approches de surveillance de l’intégrité des structures ont été proposées pour permettre un contrôle continu de l’état des structures en intégrant à celle-ci des capteurs intelligents. En effet, ce contrôle continu doit être effectué pour s’assurer du bon fonctionnement de celles-ci car la présence d’un défaut dans la structure peut aboutir à un accident catastrophique. Cependant, la variation des conditions environnementales et opérationnelles (CEO) dans lesquelles la structure évolue, impacte sévèrement les signaux collectés ce qui induit parfois une mauvaise interprétation de la présence du défaut dans la structure. Dans ce travail de thèse, l’application des méthodes d’apprentissage statistiques classiques a été envisagée dans le cas des structures tubulaires. Ici, les effets des paramètres de mesures sur la robustesse de ces méthodes ont été investiguées. Ensuite, deux approches ont été proposées pour remédier aux effets des CEO. La première approche suppose que la base de données des signaux de référence est suffisamment riche en variation des CEO. Dans ce cas, une estimation parcimonieuse du signal mesuré est calculée. Puis, l’erreur d’estimation est utilisée comme indicateur de défaut. Tandis que la deuxième approche est utilisée dans le cas où la base de données des signaux des références contient une variation limitée des CEO mais on suppose que celles-ci varient lentement. Dans ce cas, une mise à jour du modèle de l’état sain est effectuée en appliquant l’analyse en composante principale (PCA) par fenêtre mobile. Dans les deux approches, la localisation du défaut a été assurée en utilisant une fenêtre glissante sur le signal provenant de l’état endommagé.To ensure better working conditions of civil and engineering structures, inspections must be made on a regular basis. However, these inspections could be labor-intensive and cost-consuming. In this context, structural health monitoring (SHM) systems using permanently attached transducers were proposed to ensure continuous damage diagnostic of these structures. In SHM, damage detection is generally based on comparison between the healthy state signals and the current signals. Nevertheless, the environmental and operational conditions will have an effect on the healthy state signals. If these effects are not taken into account they would result in false indication of damage (false alarm). In this thesis, classical machine learning methods used for damage detection have been applied in the case of pipelines. The effects of some measurements parameters on the robustness of these methods have been investigated. Afterthat, two approaches were proposed for damage diagnostic depending on the database of reference signals. If this database contains large variation of these EOCs, a sparse estimation of the current signal is calculated. Then, the estimation error is used as an indication of the presence of damage. Otherwise, if this database is acquired at limited range of EOCs, moving window PCA can be applied to update the model of the healthy state provided that the EOCs show slow and continuous variation. In both approaches, damage localization was ensured using a sliding window over the damaged pipe signal

Sparse estimation based monitoring method for damage detection and localization: A case of study

International audienceThis paper suggests a Structural Health Monitoring (SHM) method for damage detection and localization in pipeline. The baseline signals, used in SHM, could change due to the variation of environmental and operational conditions (EOCs). Hence, the damage detection method could give rise to false alarm. In this study, this issue is addressed by selecting from the database of reference signals those with similar or very close EOCs. Such an operation can be performed by calculating a sparse estimation of the current signal. The estimation error is used as an indication of the presence of damage. Actually, a damage signal will be characterized by a high estimation error compared to that of a healthy signal. The damage location is obtained by calculating the estimation error on a sliding window over the damaged state signal. This method was tested on signals collected on a 6 m pipeline segment placed in a workshop under natural temperature variations. Results have shown that the created damage was successfully detected and localized

SVM pour une meilleure classification des données de monitoring par ondes guidées

International audienceCet article s’initie dans le cadre du monitoring des structures par le biais de la technique des ondes ultrasonores guidées. Il porte sur la classification des données de cette technique par le biais des machines à vecteurs de supports, dont la fiabilité est conditionnée par la sélection ciblée du séparateur optimal. Deux types de séparateur existent: linéaire et non-linéaire. Différents algorithmes ont été antérieurement développés et peuvent être trouvés dans la littérature. La présente étude vise à appliquer ces algorithmes sur des données expérimentales de monitoring, par le biais de la technique des ondes ultrasonores guidées, dans l’objectif d’identifier le séparateur le plus pertinent afin de réduire voire annuler complètement les fausses alarmes et fiabiliser ainsi au mieux le monitoring in-situ

Damage detection and localization in pipelines under non stationary environment variation using sparse estimation of monitoring signals

International audienceThis paper suggests a method for damage detection and localization in pipelines. It was proposed to address the problem of the effects of environmental and operational conditions on the monitoring signals. It consists of establishing a model on demand of the reference state of the structure whenever a new measured signal is presented. This model is constructed by calculating sparse estimation of the actual signal. In this case, damage detection is accomplished by using the estimation error as a damage index. To localize the damage, the idea is to calculate the estimation error only on a sliding window over the signal from the damaged pipeline. The proposed method is evaluated on a pipeline segment placed in a facility where the temperature is the only environmental factor that varies during the monitoring period. Damage was created at the end of the monitoring period by removing material from the inside of the pipeline in order to simulate corrosion. Results have shown that the damage was detected successfully and localized with a minor error

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