Ensemble of feature extraction methods to improve the structural damage classification in a wind turbine foundation

The condition monitoring of offshore wind power plants is an important topic that remains open. This monitoring aims to lower the maintenance cost of these plants. One of the main components of the wind power plant is the wind turbine foundation. This study describes a data-driven structural damage classification methodology applied in a wind turbine foundation. A vibration response was captured in the structure using an accelerometer network. After arranging the obtained data, a feature vector of 58 008 features was obtained. An ensemble approach of feature extraction methods was applied to obtain a new set of features. Principal Component Analysis (PCA) and Laplacian eigenmaps were used as dimensionality reduction methods, each one separately. The union of these new features is used to create a reduced feature matrix. The reduced feature matrix is used as input to train an Extreme Gradient Boosting (XGBoost) machine learning-based classification model. Four different damage scenarios were applied in the structure. Therefore, considering the healthy structure, there were 5 classes in total that were correctly classified. Five-fold cross validation is used to obtain a final classification accuracy. As a result, 100% of classification accuracy was obtained after applying the developed damage classification methodology in a wind-turbine offshore jacket-type foundation benchmark structure

A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications

Las estructuras civiles y militares son susceptibles y vulnerables a sufrir daños debido a las condiciones ambientales y operativas. Por lo tanto, la implementación de tecnología para brindar soluciones robustas en la identificación de daños (mediante el uso de señales adquiridas directamente de la estructura) es un requisito para reducir los costos operativos y de mantenimiento. En este sentido, el uso de sensores permanentemente adheridos a las estructuras ha demostrado una gran versatilidad y beneficio ya que el sistema de inspección puede ser automatizado. Esta automatización se lleva a cabo con tareas de procesamiento de señales con el objetivo de un análisis de reconocimiento de patrones. Este trabajo presenta la descripción detallada de un sistema de monitoreo de salud estructural (SHM) basado en el uso de un sistema activo piezoeléctrico (PZT). El sistema SHM incluye: (i) el uso de una red de sensores piezoeléctricos para excitar la estructura y recoger la respuesta dinámica medida, en varias fases de actuación; (ii) organización de datos; (iii) técnicas avanzadas de procesamiento de señales para definir los vectores de características; y finalmente; (iv) el algoritmo del vecino más cercano como un enfoque de aprendizaje automático para clasificar diferentes tipos de daños. Se incluye y analiza una descripción de la configuración experimental, la validación experimental y una discusión de los resultados de dos estructuras diferentesCivil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational and maintenance costs. In this sense, the use of sensors permanently attached to the structures has demonstrated a great versatility and benefit since the inspection system can be automated. This automation is carried out with signal processing tasks with the aim of a pattern recognition analysis. This work presents the detailed description of a structural health monitoring (SHM) system based on the use of a piezoelectric (PZT) active system. The SHM system includes: (i) the use of a piezoelectric sensor network to excite the structure and collect the measured dynamic response, in several actuation phases; (ii) data organization; (iii) advanced signal processing techniques to define the feature vectors; and finally; (iv) the nearest neighbor algorithm as a machine learning approach to classify different kinds of damage. A description of the experimental setup, the experimental validation and a discussion of the results from two different structures are included and analyzed.Q2Grupo de Investigación en Diseño, Análisis y Desarrollo de Sistemas de Ingeniería -GIDA

A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications

Civil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational and maintenance costs. In this sense, the use of sensors permanently attached to the structures has demonstrated a great versatility and benefit since the inspection system can be automated. This automation is carried out with signal processing tasks with the aim of a pattern recognition analysis. This work presents the detailed description of a structural health monitoring (SHM) system based on the use of a piezoelectric (PZT) active system. The SHM system includes: (i) the use of a piezoelectric sensor network to excite the structure and collect the measured dynamic response, in several actuation phases; (ii) data organization; (iii) advanced signal processing techniques to define the feature vectors; and finally; (iv) the nearest neighbor algorithm as a machine learning approach to classify different kinds of damage. A description of the experimental setup, the experimental validation and a discussion of the results from two different structures are included and analyzed

Distributed piezo electric sensor system for damage identification in structures subjected to temperature changes

El monitoreo de la salud estructural (SHM) es un área muy importante en un amplio espectro de campos y aplicaciones de ingeniería. Con un sistema SHM, es posible reducir el número de tareas de inspección no necesarias, el riesgo asociado y el costo de mantenimiento en una amplia gama de estructuras durante su vida útil. Uno de los problemas en la detección y clasificación de daños son los constantes cambios en las condiciones operativas y ambientales. Los pequeños cambios de estas condiciones pueden ser considerados por el sistema SHM como daños aunque la estructura esté sana. Se han desarrollado y reportado en la literatura varias aplicaciones para el monitoreo de estructuras, y algunas de ellas incluyen técnicas de compensación de temperatura. Sin embargo, en aplicaciones reales, las tecnologías de procesamiento digital han demostrado su valor al: (i) ofrecer una forma muy interesante de adquirir información de las estructuras bajo prueba; (ii) aplicar metodologías para brindar un análisis robusto; y (iii) realizar una identificación de daños con una precisión práctica y útil. Este trabajo muestra la implementación de un sistema SHM basado en el uso de sensores piezoeléctricos (PZT) para inspeccionar una estructura sujeta a cambios de temperatura. La metodología incluye el uso de análisis multivariante, fusión de datos de sensores y enfoques de aprendizaje automático. La metodología se prueba y evalúa con estructuras de aluminio y compuestos que están sujetas a variaciones de temperatura. Los resultados muestran que los daños se pueden detectar y clasificar en todos los casos a pesar de los cambios de temperatura. y (iii) realizar una identificación de daños con una precisión práctica y útil. Este trabajo muestra la implementación de un sistema SHM basado en el uso de sensores piezoeléctricos (PZT) para inspeccionar una estructura sujeta a cambios de temperatura. La metodología incluye el uso de análisis multivariante, fusión de datos de sensores y enfoques de aprendizaje automático. La metodología se prueba y evalúa con estructuras de aluminio y compuestos que están sujetas a variaciones de temperatura. Los resultados muestran que los daños se pueden detectar y clasificar en todos los casos a pesar de los cambios de temperatura. y (iii) realizar una identificación de daños con una precisión práctica y útil. Este trabajo muestra la implementación de un sistema SHM basado en el uso de sensores piezoeléctricos (PZT) para inspeccionar una estructura sujeta a cambios de temperatura. La metodología incluye el uso de análisis multivariante, fusión de datos de sensores y enfoques de aprendizaje automático. La metodología se prueba y evalúa con estructuras de aluminio y compuestos que están sujetas a variaciones de temperatura. Los resultados muestran que los daños se pueden detectar y clasificar en todos los casos a pesar de los cambios de temperatura. La metodología incluye el uso de análisis multivariante, fusión de datos de sensores y enfoques de aprendizaje automático. La metodología se prueba y evalúa con estructuras de aluminio y compuestos que están sujetas a variaciones de temperatura. Los resultados muestran que los daños se pueden detectar y clasificar en todos los casos a pesar de los cambios de temperatura. La metodología incluye el uso de análisis multivariante, fusión de datos de sensores y enfoques de aprendizaje automático. La metodología se prueba y evalúa con estructuras de aluminio y compuestos que están sujetas a variaciones de temperatura. Los resultados muestran que los daños se pueden detectar y clasificar en todos los casos a pesar de los cambios de temperaturaStructural health monitoring (SHM) is a very important area in a wide spectrum of fields and engineering applications. With an SHM system, it is possible to reduce the number of non-necessary inspection tasks, the associated risk and the maintenance cost in a wide range of structures during their lifetime. One of the problems in the detection and classification of damage are the constant changes in the operational and environmental conditions. Small changes of these conditions can be considered by the SHM system as damage even though the structure is healthy. Several applications for monitoring of structures have been developed and reported in the literature, and some of them include temperature compensation techniques. In real applications, however, digital processing technologies have proven their value by: (i) offering a very interesting way to acquire information from the structures under test; (ii) applying methodologies to provide a robust analysis; and (iii) performing a damage identification with a practical useful accuracy. This work shows the implementation of an SHM system based on the use of piezoelectric (PZT) sensors for inspecting a structure subjected to temperature changes. The methodology includes the use of multivariate analysis, sensor data fusion and machine learning approaches. The methodology is tested and evaluated with aluminum and composite structures that are subjected to temperature variations. Results show that damage can be detected and classified in all of the cases in spite of the temperature changesQ2Grupo de Investigación en Diseño, Análisis y Desarrollo de Sistemas de Ingeniería -GIDA

Locally Linear Embedding as Nonlinear Feature Extraction to Discriminate Liquids with a Cyclic Voltammetric Electronic Tongue

Electronic tongues are devices used in the analysis of aqueous matrices for classification or quantification tasks. These systems are composed of several sensors of different materials, a data acquisition unit, and a pattern recognition system. Voltammetric sensors have been used in electronic tongues using the cyclic voltammetry method. By using this method, each sensor yields a voltammogram that relates the response in current to the change in voltage applied to the working electrode. A great amount of data is obtained in the experimental procedure which allows handling the analysis as a pattern recognition application; however, the development of efficient machine-learning-based methodologies is still an open research interest topic. As a contribution, this work presents a novel data processing methodology to classify signals acquired by a cyclic voltammetric electronic tongue. This methodology is composed of several stages such as data normalization through group scaling method and a nonlinear feature extraction step with locally linear embedding (LLE) technique. The reduced-size feature vector input to a k-Nearest Neighbors (k-NN) supervised classifier algorithm. A leave-one-out cross-validation (LOOCV) procedure is performed to obtain the final classification accuracy. The methodology is validated with a data set of five different juices as liquid substances.Two screen-printed electrodes voltametric sensors were used in the electronic tongue. Specifically the materials of their working electrodes were platinum and graphite. The results reached an 80% classification accuracy after applying the developed methodology

Un sistema de fusión de datos de sensores basado en la clasificación de patrones de vecinos más cercanos k para aplicaciones de monitoreo de salud estructural

Las estructuras civiles y militares son susceptibles y vulnerables a sufrir daños debido a las condiciones ambientales y operativas. Por lo tanto, la implementación de tecnología para brindar soluciones robustas en la identificación de daños (mediante el uso de señales adquiridas directamente de la estructura) es un requisito para reducir los costos operativos y de mantenimiento. En este sentido, el uso de sensores permanentemente adheridos a las estructuras ha demostrado una gran versatilidad y beneficio ya que el sistema de inspección puede ser automatizado. Esta automatización se lleva a cabo con tareas de procesamiento de señales con el objetivo de un análisis de reconocimiento de patrones. Este trabajo presenta la descripción detallada de un sistema de monitoreo de salud estructural (SHM) basado en el uso de un sistema activo piezoeléctrico (PZT). El sistema SHM incluye: (i) el uso de una red de sensores piezoeléctricos para excitar la estructura y recoger la respuesta dinámica medida, en varias fases de actuación; (ii) organización de datos; (iii) técnicas avanzadas de procesamiento de señales para definir los vectores de características; y finalmente; (iv) el algoritmo del vecino más cercano como un enfoque de aprendizaje automático para clasificar diferentes tipos de daños. Se incluye y analiza una descripción de la configuración experimental, la validación experimental y una discusión de los resultados de dos estructuras diferentesCivil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational and maintenance costs. In this sense, the use of sensors permanently attached to the structures has demonstrated a great versatility and benefit since the inspection system can be automated. This automation is carried out with signal processing tasks with the aim of a pattern recognition analysis. This work presents the detailed description of a structural health monitoring (SHM) system based on the use of a piezoelectric (PZT) active system. The SHM system includes: (i) the use of a piezoelectric sensor network to excite the structure and collect the measured dynamic response, in several actuation phases; (ii) data organization; (iii) advanced signal processing techniques to define the feature vectors; and finally; (iv) the nearest neighbor algorithm as a machine learning approach to classify different kinds of damage. A description of the experimental setup, the experimental validation and a discussion of the results from two different structures are included and analyzed.Q2Grupo de Investigación en Diseño, Análisis y Desarrollo de Sistemas de Ingeniería -GIDA

Development of a Pattern Recognition Tool for the Classification of Electronic Tongue Signals Using Machine Learning

Electronic tongue type sensor arrays are made of different materials with the property of capturing signals independently by each sensor. The signals captured when conducting electrochemical tests often have high dimensionality, which increases when performing the data unfolding process. This unfolding process consists of arranging the data coming from different experiments, sensors, and sample times, thus the obtained information is arranged in a two-dimensional matrix. In this work, a description of a tool for the analysis of electronic tongue signals is developed. This tool is developed in Matlab® App Designer, to process and classify the data from different substances analyzed by an electronic tongue type sensor array. The data processing is carried out through the execution of the following stages: (1) data unfolding, (2) normalization, (3) dimensionality reduction, (4) classification through a supervised machine learning model, and finally (5) a cross-validation procedure to calculate a set of classification performance measures. Some important characteristics of this tool are the possibility to tune the parameters of the dimensionality reduction and classifier algorithms, and also plot the two and three-dimensional scatter plot of the features after reduced the dimensionality. This to see the data separability between classes and compatibility in each class. This interface is successfully tested with two electronic tongue sensor array datasets with multi-frequency large amplitude pulse voltammetry (MLAPV) signals. The developed graphical user interface allows comparing different methods in each of the mentioned stages to find the best combination of methods and thus obtain the highest values of classification performance measures

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

Intelligent electronic tongue system for the classification of genuine and false honeys

ABSTRACTHoney quality is a global concern since this product is highly susceptible to adulteration, given its competitive price. As a reliable strategy for honey authenticity determination, this work introduces an intelligent classification system that considers the pattern recognition point of view to develop an economical and quick analytical method to identify and differentiate genuine from adulterated honey. This work used an electronic tongue composed of three working electrodes of carbon, platinum, and gold. The system used Cyclic voltammetry to obtain data from 50 genuine and 50 adulterated honey samples. Subsequently, the system used multivariate data analysis using a pattern recognition methodology composed of three big stages, including data organization and normalization, dimensionality reduction, and k-Nearest Neighbors (k-NN) as a classification method. The process was validated with the Leave One Out Cross Validation technique (LOOCV), reaching a classification accuracy performance of 100%. The results show that it was possible the development of a combined methodology between analytical tools and chemometrics for an in-situ, quick and efficient authenticity honey evaluation