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Phased Array Damage Detection and Damage Classification in Guided Wave Structural Health MonitoringKim, Daewon 26 May 2011 (has links)
Although nondestructive evaluation techniques have been implemented in many industry fields and proved to be useful, they are generally expensive, time consuming, and the results may not always be reliable. To overcome these drawbacks, structural health monitoring (SHM) systems has received significant attention in the past two decades. As structural systems are becoming more complicated and new materials are being developed, new methodologies, theories, and approaches in SHM have been developed for damage detection, diagnosis, and prognosis.
Among the methods developed, the guided Lamb wave based SHM can be a promising technique for damage evaluation since it provides reliable damage information through signals propagating over large distance with little loss of amplitude. While this method is effective for damage assessment, the guided Lamb wave contains complicated mode characteristics, i.e. an infinite number of wave modes exist and these modes are generally dispersive. For this reason, a minimum number of wave modes and various signal processing algorithms are implemented to obtain better signal interpretations.
Phased array beamsteering is an effective means for damage detection in guided Lamb wave SHM systems. Using this method, the wave energy can be focused at localized directions or areas by controlled excitation time delay of each array element. In this research, two types of transducers are utilized as phased array elements to compare beamsteering characteristics. Monolithic piezoceramic (PZT) transducers are investigated for beamsteering by assuming omnidirectional point sources for each actuator. MacroFiber Composite (MFC) transducers with anisotropic actuation are also studied, considering the wave main lobe width, main lobe magnitude, and side lobe levels. Analysis results demonstrate that the MFC phased arrays perform better than the PZT phased arrays for a range of beamsteering angles and have reduced main lobe width and side lobe levels. Experiments using the PZT and MFC phased arrays on an aluminum plate are also performed and compared to the analysis results.
A time-frequency signal processing algorithm coupled with a machine learning method can form a robust damage diagnostic system. Four types of such algorithms, i.e. short time Fourier transform, Wigner-Ville distribution, wavelet transform, and matching pursuit, are investigated to select an appropriate algorithm for damage classification, and a spectrogram based on short time Fourier transform is adopted for its suitability. A machine learning algorithm called Adaboost is chosen due to its effectiveness and high accuracy performance. The classification is preformed using spectrograms and Adaboost for crack and corrosion damages. Artificial cracks and corrosions are created in Abaqus® to obtain the training samples consist of spectrograms. Several beam experiments in laboratory and additional simulations are also performed to get the testing samples for Adaboost. The analysis results show that not only correct damage classification is possible, but the confidence levels of each sample are acquired. / Ph. D.
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Sensing and Knowledge Mining for Structural Health ManagementJanuary 2011 (has links)
abstract: Current economic conditions necessitate the extension of service lives for a variety of aerospace systems. As a result, there is an increased need for structural health management (SHM) systems to increase safety, extend life, reduce maintenance costs, and minimize downtime, lowering life cycle costs for these aging systems. The implementation of such a system requires a collaborative research effort in a variety of areas such as novel sensing techniques, robust algorithms for damage interrogation, high fidelity probabilistic progressive damage models, and hybrid residual life estimation models. This dissertation focuses on the sensing and damage estimation aspects of this multidisciplinary topic for application in metallic and composite material systems. The primary means of interrogating a structure in this work is through the use of Lamb wave propagation which works well for the thin structures used in aerospace applications. Piezoelectric transducers (PZTs) were selected for this application since they can be used as both sensors and actuators of guided waves. Placement of these transducers is an important issue in wave based approaches as Lamb waves are sensitive to changes in material properties, geometry, and boundary conditions which may obscure the presence of damage if they are not taken into account during sensor placement. The placement scheme proposed in this dissertation arranges piezoelectric transducers in a pitch-catch mode so the entire structure can be covered using a minimum number of sensors. The stress distribution of the structure is also considered so PZTs are placed in regions where they do not fail before the host structure. In order to process the data from these transducers, advanced signal processing techniques are employed to detect the presence of damage in complex structures. To provide a better estimate of the damage for accurate life estimation, machine learning techniques are used to classify the type of damage in the structure. A data structure analysis approach is used to reduce the amount of data collected and increase computational efficiency. In the case of low velocity impact damage, fiber Bragg grating (FBG) sensors were used with a nonlinear regression tool to reconstruct the loading at the impact site. / Dissertation/Thesis / Ph.D. Aerospace Engineering 2011
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Sanierung von GewölbebrückenPelka, Conrad 09 November 2022 (has links)
Historische Gewölbebrücken im Netz der Deutschen Bahn sind sowohl wichtige Infrastrukturobjekte als auch prägende Kulturgüter. Viele der über 6.000 Gewölbebrücken befinden sich in einem überalterten Zustand und sind für den Abbruch vorgesehen. Eine Teilerneuerung dient weitestgehend dem Erhalt der tragenden Struktur der Bestandsbrücken. Vergleichsbetrachtungen an Gewölben mit einem Bogen und unterschiedlichen Bogenformen sind Voraussetzung für die Ableitung statisch und konstruktiv sinnvoller, ressourcenschonender, wirtschaftlicher sowie baukulturell verträglicher Sanierungskonzepte. Diese stehen immer im Zusammenhang mit den vorliegenden Schadensbildern.
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Deep Learning with Vision-based Technologies for Structural Damage Detection and Health MonitoringBai, Yongsheng 08 December 2022 (has links)
No description available.
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