In this paper we present the application of two damage detection methods to a laboratory tower. The first method is based on subspace identification. The second one is based on AutoRegressive modeling of the signals involved. Both methods are tested in a tower demonstrator simulating a wind turbine. They are able to correctly detect damage in the structure that is simulated by loosening some of the bolts in the joints. The results show that the first method is computationally more efficient, but the results are more stable with the second method.
Combustion control requires visible photodetectors to sense the CH* CL emission at 430 nm that combined with a visible-blind UV photodetector allows us to obtain the OH*/CH* ratio. UV-visible P-InGaN/GaN multiple quantum well-N photodiodes with 15-18 mm2 areas were fabricated to conduct OH* (308 nm) and CH* CL detection without external filters. Bandpass detectors at 230-390 nm and 360-450 nm presented linear responses over five decades and rejection ratios >10(3) at 430 and 308 nm, respectively. A full optical sensor system was built and detectors operated at 120 degrees C in a combustion chamber, showing linear responses within the dynamic range, maximum signal-to-noise ratios of 103 and response times of <1 s. An exponential association dependence between the optical OH*/CH* CL signals and the gas/air ratios was found.
Damage Detection problem in Structural Health Monitoring (SHM) is widely studied by many researchers, therefore lots of damage detection algorithms can be found in the literature. Feature Selection / Extraction methods are essential in the accuracy of these algorithms, they provide the suitable data to be used. The main goal of this work is to improve the input data to be the most representative for the damage detection problem. This is done using different Feature Selection / Extraction methods (PCA, UmRMR, and a combination of both). After taking the representative features, the results are tested using a damage detection method; the NullSpace in this case. The data has been collected from a Laboratory Offshore tower model. The different results are compared (different preprocessing vs Raw data) and these show how the correct preselection of the data can improve damage detection.
In this chapter a complete methodology for a SHM damage detection solution is explained, and how this is validated in a laboratory tower model. Several methodologies are proposed for the typical process of SHM. Starting with sensor placement (the best possible sensor locations are found), selecting the more representative data, classifying the different environmental and operational conditions, applying a damage detection methodology, including sensor fault detection. 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.
This paper presents a method to detect and identify damage in a laboratory offshore wind turbine support structure. The structure consists of three different parts: the jacket, the tower and the nacelle. The jacket is a lattice structure joined with several bolts. The tower consists of three different sections joined by bolts. The nacelle is composed of a single piece. The different parts are also joined with bolts. The damage in the structure is simulated by loosening some of the bolts in the jacket. Two damage detection algorithms, namely AutoRegressive methods and NullSpace methods, have been tested in a primitive variation of this structure without the jacket, obtaining good results. In this paper we present the application of the last damage detection method to the new structure with the jacket and an extension to identification of the damage.
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