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

Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study

In Laser Wire Additive Manufacturing (LWAM), the final geometry is produced using the layer-by-layer deposition (beads principle). To achieve good geometrical accuracy in the final product, proper implementation of the bead geometry is essential. For this reason, the paper focuses on this process and proposes a layer geometry (width and height) prediction model to improve deposition accuracy. More specifically, a machine learning regression algorithm is applied on several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. To validate the effectiveness of the proposed approach, a test split validation strategy was applied to train and validate the machine learning models. The results show a particular evolutionary trend and confirm that the process parameters have a direct influence on the bead geometry, and so, too, on the final part. Several deposition parameters have been found to obtain an accurate prediction model with low errors and good layer deposition. Finally, this study indicates that the machine learning approach can efficiently be used to predict the bead geometry and could help later in designing a proper controller in the LWAM process

Parametric modeling approach in laser wire additive manufacturing process

peer reviewedIn recent years, the evolution of computer-aided design technology has established a new design method to improve digital model creations. The new design method is called Parametric Modeling. In this paper, 3D metallic models are printed using a novel Parametric Modeling approach. The goal of this approach is to use parametric design features to simulate and print 3D metallic objects using Laser Wire Additive Manufacturing (LWAM) process. The proposed approach includes a pattern creation and robot targets assignment while considering several process requirements of LWAM and the robot system. This technique will allow the development of an adaptive robot toolpath for a good deposition process. Finally, a wall, a cylinder, and a complex shape were simulated and deposited to validate the proposed approach. The results show that the approach is feasible, adaptive, and can enhance 3D metallic print in the LWAM process

(International Congress on Ultrasonics, Universidad de Santiago de Chile, January 2009)

A tensor Hankel transform'' (THT) is defined for vector fields, such as displacement, and second-order tensor fields, such as stress or strain. The THT establishes a bijection between the real space and the wave-vector domain, and, remarkably, cannot be reduced to a scalar transform applied separately to each component. One of the advantages of this approach is that some standard elasticity problems can be concisely rewritten by applying this tensor integral transform coupled with an azimuthal Fourier series expansion. A simple and compact formulation of the boundary conditions is also achieved. Thanks to the THT, we obtain for each azimuthal wavenumber and each azimuthal direction exactly the same wave equation as for a standard 2D model of elastic wave propagation. Thus, waves similar to the standard plane P, SV and SH waves are naturally found. Lastly, the THT is used to calculate the ultrasonic field in an isotropic cylindrical leaky waveguide, the walls of which radiating into a surrounding elastic medium, by using a standard scattering approach

Ultrasonic guided waves for reinforced plastics safety

International audienceThe application of ultrasonic guided waves (UGW) to the field of preventive maintenance of composite structures is in continuous increase. Today, UGW is taking an important economical place, especially in the fields of transport and nuclear technology, where the safety of individuals is of higher importance than financial cost

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

Towards an Ultrasonic Guided Wave Procedure for Health Monitoring of Composite Vessels: Application to Hydrogen-Powered Aircraft

International audienceThis paper presents an overview and description of the approach to be used to investigate the behavior and the defect sensitivity of various ultrasonic guided wave (UGW) modes propagating specifically in composite cylindrical vessels in the framework of the safety of hydrogen energy transportation such as hydrogen-powered aircrafts. These structures which consist of thick and multi-layer composites are envisioned for housing hydrogen gas at high pressures. Due to safety concerns associated with a weakened structure, structural health monitoring techniques are needed. A procedure for optimizing damage detection in these structural types is presented. It is shown that a finite element method can help identify useful experimental parameters including frequency range, excitation type, and receiver placement

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

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