Acoustic emission testing and acousto-ultrasonics for structural health monitoring

The global trends in the construction of modern structures require the integration of sensors together with data recording and analysis modules so that its integrity can be continuously monitored for safe-life, economic and ecological reasons. This process of measuring and analysing the data from a distributed sensor network all over a structural system in order to quantify its condition is known as structural health monitoring (SHM). The research presented in this thesis is motivated by the need to improve the inspection capabilities and reliability of SHM systems based on ultrasonic guided waves with focus on the acoustic emission and acousto-ultrasonics techniques. The use of a guided wave-based approach is driven by the fact that these waves are able to propagate over relatively long distances, interact sensitively with and/or being related to different types of defect. The main emphasis of the thesis is concentrated on the development of different methodologies based on signal analysis together with the fundamental understanding of wave propagation for the solution of problems such as damage detection, localisation and identification. The behaviour of guided waves for both techniques is predicted through modelling in order to investigate the characteristics of the modes being propagated throughout the evaluated structures and support signal analysis. The validity of the developed model is extensively investigated by contrasting numerical simulations and experiments. In this thesis special attention is paid to the development of efficient SHM methodologies. This fact requires robust signal processing techniques for the correct interpretation of the complex ultrasonic waves. Therefore, a variety of existing algorithms for signal processing and pattern recognition are evaluated and integrated into the different proposed methodologies. Additionally, effects such as temperature variability and operational conditions are experimentally studied in order to analyse their influence on the performance of developed methodologies. At the end, the efficiency of these methodologies are experimentally evaluated in diverse isotropic and anisotropic composite structures.Nach den heutigen Standards zur Konstruktion moderner Leichtbaustrukturen ist es zur Strukturüberwachung aufgrund von wirtschaftlichen, ökologischen und Sicherheitsaspekten unerlässlich, Sensoren und Module zur Datenspeicherung und –analyse in diese Strukturen zu integrieren. Den Prozess der Strukturüberwachung anhand der Messung und Analyse von Daten aus einem dezentralen Sensornetzwerk wird als „Structural Health Monitoring (SHM)“ bezeichnet. Die vorliegende Arbeit und die darin vorgestellten Untersuchungen reagieren auf den Bedarf an verbesserter Genauigkeit und höherer Zuverlässigkeit von SHM-Systemen, die auf geführten Ultraschallwellen basieren, wobei der Fokus der Untersuchung auf Schallemissions- und Acousto-Ultraschalltechniken liegt. Da geführte Wellen lange Wege zurückzulegen können und mit hoher Empfindlichkeit und Genauigkeit auf verschiedene Schadenstypen reagieren, eignen sie sich sehr gut für die Überwachung dünnwandiger Strukturen. Der Schwerpunkt der Arbeit liegt in der Entwicklung verschiedener Methoden zur Signalanalyse zur Lösung von Problemen wie Schadenserkennung, lokalisierung und identifizierung. Dies ist nicht ohne ein grundlegendes Verständnis der Wellenausbreitungsmechanismen möglich, sodass ein Modell entwickelt wird, anhand dessen die Charakteristiken der angeregten Moden sowie die Wellenausbreitung in den zu untersuchenden Strukturen analysiert werden können, um so die Signalanalyse zu unterstützen. Die Validität des entwickelten Modells wird eingehend anhand von verschiedenen numerischen Simulationen und Experimenten untersucht. Um besonders effiziente Methoden des SHMs zu entwickeln, sind robuste Signalverarbeitungstechniken zur zuverlässigen Interpretation komplexer Ultraschallwellen notwending. Aus diesem Grund erfolgt die Auswertung einer Vielzahl existierender Algorithmen zur Signalverarbeitung und Mustererkennung, die in die hier vorgestellten Methoden integriert werden. Des Weiteren wird experimentell untersucht, welchen Einfluss Effekte wie Temperaturschwankungen und Betriebsbedingungen auf diese Methoden haben. Abschließend wird experimentell die Effizienz der entwickelten Methoden bei der Überwachung diverser isotroper und anisotroper Faserverbundstrukturen nachgewiesen

Damage Detection in Metallic Beams from Dynamic Strain Measurements under Different Load Cases by Using Automatic Clustering and Pattern Recognition Techniques

International audienceIn general, the change in the local strain field or global stiffness caused by damage in a structure is very small and the strain field tends to homogenize very quickly in the field close to the defect. Moreover, other environmental effects can fade the slight changes in the strain field. Only by comparing the response of the structure at several points some information about damage may be unveiled. By means of pattern recognition techniques based on the strain field, this task can be achieved. This is the basis of the strain measurements data-driven models. The main limitation of the strain field pattern recognition techniques lies in the susceptibility of the strain field to change depending on the load conditions. In the case of dynamic loads, this may reflect even a greater limitation. Robust automated techniques are required to manage these limitations. In first instance, automatic clustering techniques are needed so that data can be classified according to the load conditions and secondly, a dimensional reduction technique is needed in order to obtain patterns that often underlie from data. Within the context of this paper, a combination of Local Density-based Simultaneous Two-Level (DS2L-SOM) Clustering based on Self-Organizing Maps (SOM) and Principal Components Analysis (PCA) is proposed in order to firstly, classify load conditions and secondly, perform strain field pattern recognition. The clustering technique is the basis for an Optimal Baseline Selection. An experimental validation of the technique is discussed in this paper, comparing damages of different sizes and positions in an aluminum beam, under a set of combined loads under dynamic conditions. Strains were measured at several points by using Fiber Bragg Gratings

A Pattern Recognition Approach for Damage Detection and Temperature Compensation in Acousto-Ultrasonics

International audienceThe global trends in the construction of modern structures require the integration of sensors together with data recording and analysis modules so that their integrity can be continuously monitored for safe-life, economic and ecological reasons. This process of measuring and analysing the data from a distributed sensor network all over a structural system in order to quantify its condition is known as structural health monitoring (SHM). Guided ultrasonic wave-based techniques are increasingly being adapted and used in several SHM systems which benefit from built&#8208,in transduction, large inspection ranges, and high sensitivity to small flaws. However, for reliable health monitoring, much information regarding the innate characteristics of the sources and their propagation is essential. Moreover, any SHM system which is expected to transition to field operation must take into account the influence of environmental and operational changes which cause modifications in the stiffness and damping of the structure and consequently modify its dynamic behaviour. On that account, special attention is paid in this paper to the development of an efficient SHM methodology where robust signal processing and pattern recognition techniques are integrated for the correct interpretation of complex ultrasonic waves within the context of damage detection and identification. The methodology is based on an acousto-ultrasonics technique where the discrete wavelet transform is evaluated for feature extraction and selection, linear principal component analysis for data-driven modelling and self-organizing maps for a two-level clustering under the principle of local density. At the end, the methodology is experimentally demonstrated and results show that all the damages were detectable and i

Damage assessment in a stiffened composite panel using non-linear data-driven modelling and ultrasonic guided waves

Structural components made of composite materials are being used more often in aerospace and aeronautic structures due to their well-known properties such as high mass specific stiffness and strength. However, their application also increases the analysis complexity of such structures. Structural health monitoring (SHM) systems for these structures aim to determine the status of the system in real time such that a longer safe life and lower operational costs can be guaranteed. On that account, this paper is concerned with the experimental validation of a structural health monitoring methodology where a damage detection and classification scheme based on an acousto-ultrasonic (AU) approach is applied to a composite panel incorporating stiffening elements using a piezoelectric active sensor network in conjunction with time-frequency multiresolution analysis and non-linear feature extraction. Therefore, structural dynamic responses from the simplified aircraft composite skin panel are collected and signal features are then extracted with a signal processing and data fusion methodology in terms of the wavelet transform technique and hierarchical non-linear principal component analysis. A critical comparison with linear feature extraction methods indicates that the proposed method outperforms the traditional linear methods for the purpose of damage classification. Additionally, results show that all the damages were detectable and classifiable, and the selected features proved capable of separating all damage conditions from the undamaged state.Postprint (published version

Damage detection in piping systems using pattern recognition techniques

The interest in the propagation of ultrasound waves in pipe-like solid waveguides arises out of several areas of the structural health monitoring (SHM) community for the detection, localization and assessment of defects as well as the prediction of remaining life in civil, mechanical, aeronautic and aerospace structures. SHM premise offers a continuous observation of the structural integrity of operational systems. This is particularly convenient, therefore, for the reduction of time and cost for maintenance without decreasing the level of safety. Some practical applications are the monitoring of pipework in gas and oil industries, suspension bridge cables, nuclear fuel cladding tubes, etc. This paper describes an approach for SHM using guided waves in pipe-like structures in terms of a pattern recognition problem. The formalism is based on a distributed piezoelectric sensor network for the detection of structural dynamic responses. Several methods for signal filtration, feature selection and extraction, and data compression of the recorded time histories are discussed and evaluated. Principal Component Analysis (PCA), Non-Linear PCA (NLPCA) and Wavelet Transform are among them. Additionally, the different clusters, corresponding to each damage level are visualized with the help of Self Organizing Maps (SOM). Tests were performed on a piping system where the properties of the proposed methods are compared and appraised with experimental pitch-catch signals between the pristine and the damaged structure

Damage detection in piping systems using pattern recognition techniques

The interest in the propagation of ultrasound waves in pipe-like solid waveguides arises out of several areas of the structural health monitoring (SHM) community for the detection, localization and assessment of defects as well as the prediction of remaining life in civil, mechanical, aeronautic and aerospace structures. SHM premise offers a continuous observation of the structural integrity of operational systems. This is particularly convenient, therefore, for the reduction of time and cost for maintenance without decreasing the level of safety. Some practical applications are the monitoring of pipework in gas and oil industries, suspension bridge cables, nuclear fuel cladding tubes, etc. This paper describes an approach for SHM using guided waves in pipe-like structures in terms of a pattern recognition problem. The formalism is based on a distributed piezoelectric sensor network for the detection of structural dynamic responses. Several methods for signal filtration, feature selection and extraction, and data compression of the recorded time histories are discussed and evaluated. Principal Component Analysis (PCA), Non-Linear PCA (NLPCA) and Wavelet Transform are among them. Additionally, the different clusters, corresponding to each damage level are visualized with the help of Self Organizing Maps (SOM). Tests were performed on a piping system where the properties of the proposed methods are compared and appraised with experimental pitch-catch signals between the pristine and the damaged structure

Damage assessment in a stiffened composite panel using non-linear data-driven modelling and ultrasonic guided waves

Structural components made of composite materials are being used more often in aerospace and aeronautic structures due to their well-known properties such as high mass specific stiffness and strength. However, their application also increases the analysis complexity of such structures. Structural health monitoring (SHM) systems for these structures aim to determine the status of the system in real time such that a longer safe life and lower operational costs can be guaranteed. On that account, this paper is concerned with the experimental validation of a structural health monitoring methodology where a damage detection and classification scheme based on an acousto-ultrasonic (AU) approach is applied to a composite panel incorporating stiffening elements using a piezoelectric active sensor network in conjunction with time-frequency multiresolution analysis and non-linear feature extraction. Therefore, structural dynamic responses from the simplified aircraft composite skin panel are collected and signal features are then extracted with a signal processing and data fusion methodology in terms of the wavelet transform technique and hierarchical non-linear principal component analysis. A critical comparison with linear feature extraction methods indicates that the proposed method outperforms the traditional linear methods for the purpose of damage classification. Additionally, results show that all the damages were detectable and classifiable, and the selected features proved capable of separating all damage conditions from the undamaged state

Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring

Improvements in computing capacity have allowed computers today to execute increasingly complex tasks. One of the main benefits of these improvements is the possibility of developing machine learning algorithms, of which the fields of application are extensive and varied. However, an area in which this type of algorithms acquires an increasing relevance is structural health monitoring (SHM), where inspection strategies and guided wave-based approaches make the evaluation of the structural conditions of an aircraft, vessel or building among others possible, by detecting and classifying existing damages. The use of sensors, data acquisition systems (DAQ) and computation has also allowed these damage detection and classification tasks to be carried out automatically. Despite today’s advances, it is still necessary to continue with the development of more robust, reliable, and low-cost structural health monitoring systems. For this reason, this work contemplates three key points: (i) the configuration of a data acquisition system for signal gathering from an an active piezoelectric (PZT) sensor network; (ii) the development of a damage classification methodology based on signal processing techniques (normalization and PCA), from which the models that describe the structural conditions of the plate are built; and (iii) the use of machine learning algorithms, more specifically, three variants of the self-organizing maps called CPANN (counterpropagation artificial neural network), SKN (supervised Kohonen) and XYF (X–Y fused Kohonen). The data obtained allowed one to carry out an experimental validation of the damage classification methodology, to determine the presence of damages in two aluminum plates of different sizes, where masses were added to change the vibrational responses captured by the sensor network and a composite (CFRP) plate with real damages, such as delamination and cracks. This classification methodology allowed one to obtain excellent results by validating the usefulness of the SKN and XYF networks in damage classification tasks, showing overall accuracies of 73.75% and 72.5%, respectively, according to the cross-validation process. These percentages are higher than those obtained in comparison with other neural networks such as: kNN, discriminant analysis, classification trees, partial least square discriminant analysis, and backpropagation neural networks, when the cross-validation process was applied

Patrimonio biocultural. Experiencias integradoras

El libro engloba diferentes perspectivas en torno al patrimonio biocultural de diferentes regiones de México, desde un contexto histórico, hasta las problemáticas político-administrativas a las que se enfrentan estás regiones. Algunos capítulos reflejan diferentes estrategias que han seguido las comunidades para rescatar ese patrimonio biocultural y avanzar hacia la sustentabilidad.El libro fue resultado del 1er Congreso Internacional Desarrollo Sustentable: Enfoques, Aplicaciones y Perspectivas. “Ambiente, Economía, Sociedad, Territorio y Educación”. Celebrado en Toluca, Estado de México.De forma particular, el cuerpo académico sobre sustentabilidad, territorio y educación, llevo a cabo una recopilación de investigaciones en diferentes líneas de trabajo entre ellas las referentes al patrimonio biocultural y la sustentabilidad. En los trabajos aceptados se tienen experiencias que integran un sin número de aspectos que relacionan al ambiente con el patrimonio.La estructura del documento se divide en ocho capítulos y en cada uno de desarrolla la experiencia integradora del investigador

Proyecto de Investigación 1 - ME166 - 202102

La asignatura se desarrolla a través de talleres en los cuales los estudiantes realizan un protocolo de investigación que servirá para desarrollar su tesis de titulación como médico cirujano. El protocolo responde a una pregunta de investigación relevante que lleva al desarrollo de una investigación con un diseño metodológico apropiado y con un nivel de calidad potencialmente publicable. Este protocolo será presentado ante un comité de ética en investigación para su revisión. El proyecto se apoya en una revisión bibliográfica exhaustiva, que de cumplir con la rigurosidad necesaria y de ser aprobado, puede ser considerado como una de las alternativas para cumplir el requisito de trabajo de investigación para obtener el grado