Design and validation of a methodology for wind energy structures health monitoring

L’objectiu de la Monitorització de la salut estructural (SHM) és la verificació de l’estat o la salut de les estructures per tal de garantir el seu correcte funcionament i estalviar en el cost de manteniment. El sistema SHM combina una xarxa de sensors connectada a l’estructura amb monitoratge continu i algoritmes específics. Es deriven diferents beneficis de l’aplicació de SHM, on trobem: coneixement sobre el comportament de l’estructura sota diferents operacions i diferents càrregues ambientals , el coneixement de l’estat actual per tal de verificar la integritat de l’estructura i determinar si una estructura pot funcionar correctament o si necessita manteniment o substitució i, per tant, reduint els costos de manteniment. El paradigma de la detecció de danys es pot abordar com un problema de reconeixement de patrons (comparació entre les dades recollides de l’estructura sense danys i l’estructura actual, per tal de determinar si hi ha algun canvi) . Hi ha moltes tècniques que poden gestionar el problema. En aquest treball s’utilitzen les dades dels acceleròmetres per desenvolupar aproximacions estadístiques utilitzant dades en temps per a la detecció dels danys en les estructures. La metodologia s’ha dissenyat per a una turbina eòlica off - shore i només s’utilitzen les dades de sortida per detectar els danys. L’excitació de la turbina de vent és induïda pel vent o per les ones del mar. La detecció de danys no és només la comparació de les dades. S’ha dissenyat una metodologia completa per a la detecció de danys en aquest treball. Gestiona dades estructurals, selecciona les dades adequades per detectar danys, i després de tenir en compte les condicions ambientals i operacionals (EOC) en el qual l’estructura està treballant, es detecta el dany mitjançant el reconeixement de patrons. Quan es parla del paradigma de la detecció de danys sempre s’ha de tenir en compte si els sensors estan funcionant correctament. Per això és molt important comptar amb una metodologia que comprova si els sensors estan sans. En aquest treball s’ha aplicat un mètode per detectar els sensors danyats i s’ha insertat en la metodologia de detecció de danys.The objective of Structural Health Monitoring (SHM) is the verification of the state or the health of the structures in order to ensure their proper performance and save on maintenance costs. The SHM system combines a sensor network attached to the structure with continuous monitoring and specific, proprietary algorithms. Different benefits are derived from the implementation of SHM, some of them are: knowledge about the behavior of the structure under different loads and different environmental changes, knowledge of the current state in order to verify the integrity of the structure and determine whether a structure can work properly or whether it needs to be maintained or replaced and, therefore, reduce maintenance costs. The paradigm of damage detection can be tackled as a pattern recognition problem (comparison between the data collected from the structure without damages and the current structure in order to determine if there are any changes). There are lots of techniques that can handle the problem. In this work, accelerometer data is used to develop statistical data driven approaches for the detection of damages in structures. As the methodology is designed for wind turbines, only the output data is used to detect damage; the excitation of the wind turbine is provided by the wind itself or by the sea waves, being those unknown and unpredictable. The damage detection strategy is not only based on the comparison of many data. A complete methodology for damage detection based on pattern recognition has been designed for this work. It handles structural data, selects the proper data for detecting damage and besides, considers the Environmental and Operational Conditions (EOC) in which the structure is operating. The damage detection methodology should always be accessed only if there is a way to probe that the sensors are correctly working. For this reason, it is very important to have a methodology that checks whether the sensors are healthy. In this work a method to detect the damaged sensors has been also implemented and embedded into the damage detection methodology.El objetivo de la Monitorización de la salud estructural (SHM) es la verificación del estado o la salud de las estructuras con el fin de garantizar su correcto funcionamiento y ahorrar en el costo de mantenimiento. El sistema SHM combina una red de sensores conectada a la estructura con monitorización continua y algoritmos específicos. Se derivan diferentes beneficios de la aplicación de SHM, donde encontramos: conocimiento sobre el comportamiento de la estructura bajo diferentes operaciones y diferentes cargas ambientales, el conocimiento del estado actual con el fin de verificar la integridad de la estructura y determinar si una estructura puede funcionar correctamente o si necesita mantenimiento o sustitución y, por lo tanto, reduciendo los costes de mantenimiento. El paradigma de la detección de daños se puede abordar como un problema de reconocimiento de patrones (comparación entre los datos recogidos de la estructura sin daños y la estructura actual, con el fin de determinar si hay algún cambio). Hay muchas técnicas que pueden manejar el problema. En este trabajo se utilizan los datos de los acelerómetros para desarrollar aproximaciones estadísticas utilizando datos en tiempo para la detección de los daños en las estructuras. La metodología se ha diseñado para una turbina eólica off-shore y sólo se utilizan los datos de salida para detectar los daños. La excitación de la turbina de viento es inducida por el viento o por las olas del mar. La detección de daños no es sólo la comparación de los datos. Se ha diseñado una metodología completa para la detección de daños en este trabajo. Gestiona datos estructurales, selecciona los datos adecuados para detectar daños, y después de tener en cuenta las condiciones ambientales y operacionales (EOC) en el que la estructura está trabajando, se detecta el daño mediante el reconocimiento de patrones. Cuando se habla del paradigma de la detección de daños siempre se debe tener en cuenta si los sensores están funcionando correctamente. Por eso es muy importante contar con una metodología que comprueba si los sensores están sanos. En este trabajo se ha aplicado un método para detectar los sensores dañados y se ha metido en la metodología de detección de dañosPostprint (published version

The 1999 Center for Simulation of Dynamic Response in Materials Annual Technical Report

Introduction: This annual report describes research accomplishments for FY 99 of the Center for Simulation of Dynamic Response of Materials. The Center is constructing a virtual shock physics facility in which the full three dimensional response of a variety of target materials can be computed for a wide range of compressive, ten- sional, and shear loadings, including those produced by detonation of energetic materials. The goals are to facilitate computation of a variety of experiments in which strong shock and detonation waves are made to impinge on targets consisting of various combinations of materials, compute the subsequent dy- namic response of the target materials, and validate these computations against experimental data

Sampling-based Program Execution Monitoring

For its high overall cost during product development, program debugging is an important aspect of system development. Debugging is a hard and complex activity, especially in time-sensitive systems which have limited resources and demanding timing constraints. System tracing is a frequently used technique for debugging embedded systems. A specific use of system tracing is to monitor and debug control-flow problems in programs. However, it is difficult to implement because of the potentially high overhead it might introduce to the system and the changes which can occur to the system behaviour due to tracing. To solve the above problems, in this work, we present a sampling-based approach to program execution monitoring which specifically helps developers trace the program execution in time-sensitive systems such as real-time applications. We build the system model and propose three theorems which determine the sampling period or the optimal in different scenarios. We also design seven heuristics and an instrumentation framework to extend the sampling period which can reduce the monitoring overhead and achieve an optimal tradeoff between accuracy and overhead introduced by instrumentation. Using this monitoring framework, we can use the information extracted through sampling to reconstruct the system state and execution paths to locate the deviation. Based on the statistically significant data, we also model the trend of the sampling period with the instrumentation steps. Based on the modelling results, we devise a scheme for predicting the number of markers we need to reach a certain sampling period. Last, we build a tool chain to instrument and monitoring the software system and further prove the soundness of our approach

Data mining as a tool for environmental scientists

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous

Artifact Removal Methods in EEG Recordings: A Review

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods

Compilation and Binary Editing for Performance and Security

Traditionally, execution of a program follows a straight and inflexible path starting from source code, extending through a compiled executable file on disk, and culminating in an executable image in memory. This dissertation enables more flexible programs through new compilation mechanisms and binary editing techniques. To assist analysis of functions in binaries, a new compilation mechanism generates data representing control flow graphs of each function. These data allow binary analysis tools to identify the boundaries of basic blocks and the types of edges between them without examining individual instructions. A similar compilation mechanism is used to create individually relocatable basic blocks that can be relocated anywhere in memory at runtime to simplify runtime instrumentation. The concept of generating relocatable program components is also applied at function-level granularity. Through link-time function relocation, unused functions in shared libraries are moved to a section that is not loaded into the memory at runtime, reducing the memory footprint of these shared libraries. Moreover, function relocation is extended to the runtime where functions are continuously moved to random addresses to thwart system intrusion attacks. The techniques presented above result in a 74% reduction in binary parsing times as well as an 85% reduction in memory footprint of the code segment of shared libraries, while simplifying instrumentation of binary code. The techniques also provide a way to make return-oriented programming attacks virtually impossible to succeed