1,068 research outputs found
Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm
Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)
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
Comparative review of methods for stability monitoring in electrical power systems and vibrating structures
This study provides a review of methods used for stability monitoring in two different fields, electrical power systems and vibration analysis, with the aim of increasing awareness of and highlighting opportunities for cross-fertilisation. The nature of the problems that require stability monitoring in both fields are discussed here as well as the approaches that have been taken. The review of power systems methods is presented in two parts: methods for ambient or normal operation and methods for transient or post-fault operation. Similarly, the review of methods for vibration analysis is presented in two parts: methods for stationary or linear time-invariant data and methods for non-stationary or non-linear time-variant data. Some observations and comments are made regarding methods that have already been applied in both fields including recommendations for the use of different sets of algorithms that have not been utilised to date. Additionally, methods that have been applied to vibration analysis and have potential for power systems stability monitoring are discussed and recommended. � 2010 The Institution of Engineering and Technology
Quickest detection in coupled systems
This work considers the problem of quickest detection of signals in a coupled
system of N sensors, which receive continuous sequential observations from the
environment. It is assumed that the signals, which are modeled a general Ito
processes, are coupled across sensors, but that their onset times may differ
from sensor to sensor. The objective is the optimal detection of the first time
at which any sensor in the system receives a signal. The problem is formulated
as a stochastic optimization problem in which an extended average Kullback-
Leibler divergence criterion is used as a measure of detection delay, with a
constraint on the mean time between false alarms. The case in which the sensors
employ cumulative sum (CUSUM) strategies is considered, and it is proved that
the minimum of N CUSUMs is asymptotically optimal as the mean time between
false alarms increases without bound.Comment: 6 pages, 48th IEEE Conference on Decision and Control, Shanghai 2009
December 16 - 1
Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques
Peer reviewedPostprin
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