Dissertationen: „Damage and detection“

1

Cockerill, Aaron. „Damage detection of rotating machinery“. Thesis, Cardiff University, 2017. http://orca.cf.ac.uk/105671/.

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Acoustic emission (AE) is an emerging technique for the condition monitoring of rotating machinery components, including both rolling element bearings and gears. Due to the high frequency range over which AE is sensitive to, AE potentially offers advantages for detection of incipient damage at an early stage of failure when compared to traditional techniques such as vibration. This thesis first investigates the effects of increased speed and load on the generation of AE within cylindrical roller bearings, and determines similarities and differences between AE and vibrational data. A traditional AE sensor was used in conjunction with a Dual Function Sensor (DFS) capable of recording both low frequency AE and vibration. It was shown that increasing speed has the greatest influence on the AE signals produced whereas the effect of load was limited. Order analysis of both AE and vibrational data also demonstrated that characteristic bearing defect frequencies are visible in the AE spectrum but not in the vibrational spectrum. Bearings with seeded defects upon the outer raceway were investigated under a fixed speed and it was found that load increased the energy within the signal frequency spectrum as the damaged increased. Two bearing life tests were also conducted, one accelerated to 12 hours and the second extended to over 2800 hours however as damage detection only occurred after significant damage had developed, it is concluded that AE of seeded defects indicate a false sensitivity. Both life tests were able to demonstrate that signal levels increase as damage propagates over the bearing raceway however it was not possible to determine any advantage of using AE over vibration. AE sensors were also applied to test rigs of increased complexity, including the monitoring a wind turbine planet bearing and a helical gear pair. AE was able to detect cracking of the shaft surface within the wind turbine bearing test rig which was mistaken for being an inner raceway failure, highlighting the difficulty in damage location. A tooth failure occurred during the testing of the helical gear pair however AE was not able to detect growing damage, instead only increasing in amplitude after the tooth had sheared off, similar to the detection from vibrational signals.

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2

Al, Jailawi Samer Saadi Hussein. „Damage detection using angular velocity“. Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6539.

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The present work introduces novel methodologies for damage detection and health monitoring of structural and mechanical systems. The new approach uses the angular velocity inside different mathematical forms, via a gyroscope, to detect, locate, and relatively quantify damage. This new approach has been shown to outperform the current state-of-the-art acceleration-based approach in detecting damage on structures. Additionally, the current approach has been shown to be less sensitive to environmental acoustic noises, which present major challenges to the acceleration-based approaches. Furthermore, the current approach has been demonstrated to work effectively on arch structures, which acceleration-based approaches have struggled to deal with. The efficacy of the new approach has been investigated through multiple forms of structural damage indices. The first methodology proposed a damage index that is based on the changes in the second spatial derivative (curvature) of the power spectral density (PSD) of the angular velocity during vibration. The proposed method is based on the output motion only and does not require information about the input forces/motions. The PSD of the angular velocity signal at different locations on structural beams was used to identify the frequencies where the beams show large magnitude of angular velocity. The curvature of the PSD of the angular velocity at these peak frequencies was then calculated. A damage index is presented that measures the differences between the PSD curvature of the angular velocity of a damaged structure and an artificial healthy baseline structure. The second methodology proposed a damage index that is used to detect and locate damage on straight and curved beams. The approach introduces the transmissibility and coherence functions of the output angular velocity between two points on a structure where damage may occur to calculate a damage index as a metric of the changes in the dynamic integrity of the structure. The damage index considers limited-frequency bands of the transmissibility function at frequencies where the coherence is high. The efficacy of the proposed angular-velocity damage-detection approach as compared to the traditional linear-acceleration damage-detection approach was tested on straight and curved beams with different chord heights. Numerical results showed the effectiveness of the angular-velocity approach in detecting damage of multiple levels. It was observed that the magnitude of the damage index increased with the magnitude of damage, indicating the sensitivity of the proposed method to damage intensity. The results on straight and curved beams showed that the proposed approach is superior to the linear-acceleration-based approach, especially when dealing with curved beams with increasing chord heights. The experimental results showed that the damage index of the angular-velocity approach outweighed that of the acceleration approach by multiple levels in terms of detecting damage. A third methodology for health-monitoring and updating of structure supports, which resemble bridges’ bearings, is introduced in this work. The proposed method models the resistance of the supports as rotational springs and uses the transmissibility and coherence functions of the output response of the angular velocity in the neighborhood of the bearings to detect changes in the support conditions. The proposed methodology generates a health-monitoring index that evaluates the level of deterioration in the support and a support-updating scheme to update the stiffness resistance of the supports. Numerical and experimental examples using beams with different support conditions are introduced to demonstrate the effectiveness of the proposed method. The results show that the proposed method detected changes in the state of the bearings and successfully updated the changes in the stiffness of the supports.

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Dissanayake, Amal S. „Electrostatic discharge damage detection method“. Thesis, Kansas State University, 1997. http://hdl.handle.net/2097/13512.

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4

Gharibnezhad, Fahit. „Robust damage detection in smart structures“. Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/277544.

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This thesis is devoted to present some novel techniques in Structural Health Monitoring (SHM). SHM is a developing field that tries to monitor structures to make sure that they remain in their desired condition to avoid any catastrophe. SHM includes different levels from damage detection area to prognosis field. This work is dedicated to the first level, which might be considered the main and most important level. New techniques presented in this work are based on different statistical and signal processing methods such as Principal Component Analysis and its robust counterpart, Wavelet Transform, Fuzzy similarity, Andrew plots, etc. These techniques are applied on the propagated waves that are activated and captured in the structure using appropriate transducers. Piezoceramic (PZT) devices are chosen in this work to capture the signals due to their special characteristics such as high performance, low energy consumption and reasonable price. To guarantee the efficiency of the suggested techniques, they are tested on different laboratory and real scale test benchmarks, such as aluminum and composite plates, fuselage, wing skeleton, tube, etc. Because of the variety of tested benchmarks, this thesis is called damage detection in smart structures. This variety may promise the ability and capability of the proposed methods on different fields such as aerospace and gas/oil industry. In addition to the normal laboratory conditions, it is shown in this work that environmental changes can affect the performance of the damage detection and wave propagation significantly. As such, there is a vital need to consider their effect. In this work, temperature change is chosen as it is one of the main environmental fluctuation factors. To scrutinize its effect on damage detection, first, the effect of temperature is considered on wave propagation and then all the proposed methods are tested to check whether they are sensitive to temperature change or not. Finally, a temperature compensation method is applied to ensure that the proposed methods are stable and robust even when structures are subjected to variant environmental conditions.
La presente tesis doctoral se dedica a la exploración y presentación de técnicas novedosas para la Monitorización y detección de defectos en estructuras (Structural Health Monitoring -SHM-) SHM es un campo actualmente en desarrollo que pretende asegurarse que las estructuras permanecen en su condición deseada para evitar cualquier catástrofe. En SHM se presentan diferentes niveles de diagnóstico, Este trabajo se concentra en el primer nivel, que se considera el más importante, la detección de los defectos. Las nuevas técnicas presentadas en esta tesis se basan en diferentes métodos estadísticos y de procesamiento de señales tales como el Análisis de Componentes Princpales (PCA) y sus variaciones robustas, Transformada wavelets, lógica difusa, gráficas de Andrew, etc. Estas técnicas de aplican sobre las ondas de vibración que se generan y se miden en la estructura utilizando trasductores apropiados. Dispositivos piezocerámicos (PZT's) se han escogido para este trabajo ya que presentan características especiales tales como: alto rendimiento, bajo consumo de energia y bajo costo. Para garantizar la eficacia de la metodología propuesta,se ha validado en diferentes laboratorios y estructuras a escala real: placas de aluminio y de material compuesto, fuselage de un avión, revestimiento del ala de un avóin, tubería, etc. Debido a la gran variedad de estructuras utilizadas, su aplicación en la industria aeroespacial y/o petrolera es prometedora. Por otra parte, los cambios ambientales pueden afectar al rendimiento de la detección de daños y propagación de la onda significativamente . En este trabajo , se estudia el efecto de las variaciones de temperatura ya que es uno de los principales factores de fluctuación del medio ambiente . Para examinar su efecto en la detección de daños, en primer lugar, todos los métodos propuestos se prueban para comprobar si son sensibles a los cambios de temperatura o no. Finalmente , se aplica un método de compensación de temperatura para garantizar que los métodos propuestos son estables y robustos incluso cuando las estructuras se someten a condiciones ambientales variantes

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Matlack, Kathryn H. „Nonlinear ultrasound for radiation damage detection“. Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51965.

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Radiation damage occurs in reactor pressure vessel (RPV) steel, causing microstructural changes such as point defect clusters, interstitial loops, vacancy-solute clusters, and precipitates, that cause material embrittlement. Radiation damage is a crucial concern in the nuclear industry since many nuclear plants throughout the US are entering the first period of life extension and older plants are currently undergoing assessment of technical basis to operate beyond 60 years. The result of extended operation is that the RPV and other components will be exposed to higher levels of neutron radiation than they were originally designed to withstand. There is currently no nondestructive evaluation technique that can unambiguously assess the amount of radiation damage in RPV steels. Nonlinear ultrasound (NLU) is a nondestructive evaluation technique that is sensitive to microstructural features such as dislocations, precipitates, and their interactions in metallic materials. The physical effect monitored by NLU is the generation of higher harmonic frequencies in an initially monochromatic ultrasonic wave, arising from the interaction of the ultrasonic wave with microstructural features. This effect is quantified with the measurable acoustic nonlinearity parameter, beta. In this work, nonlinear ultrasound is used to characterize radiation damage in reactor pressure vessel steels over a range of fluence levels, irradiation temperatures, and material composition. Experimental results are presented and interpreted with newly developed analytical models that combine different irradiation-induced microstructural contributions to the acoustic nonlinearity parameter.

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Huethwohl, Philipp Karl. „Bridge damage detection and BIM mapping“. Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/285562.

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Bridges are a vitally important part of modern infrastructure. Their condition needs to be monitored on a continuous basis in order to ensure their safety and functionality. Teams of engineers visually inspect more than half a million bridges per year in the US and the EU. There is clear evidence to suggest that they are not able to meet all bridge inspection guideline requirements. In addition, the format and storage of inspection reports varies considerably across authorities because of the lack of standardisation. The availability of a comprehensive and open digital representation of the data involved in and required for bridge inspection is an indispensable necessity for exploiting the full potential of modern digital technologies like big data exploration, artificial intelligence and database technologies. A thorough understanding of bridge inspection information requirements for reinforced concrete bridges is needed as basis for overcoming the stated problem. This work starts with a bridge inspection guideline analysis, from which an information model and a candidate binding to Industry Foundation Classes (IFC) is developed. The resulting bridge model can fully store inspection information in a standardised way which makes it easily shareable and comparable between users and standards. Then, two inspection stages for locating and classifying visual concrete defects are devised, implemented and benchmarked to support the bridge inspection process: In a first stage, healthy concrete surfaces are located and disregarded for further inspection. In a second hierarchical classification stage, each of the remaining potentially unhealthy surface areas is classified into a specific defect type in accordance with bridge inspection guidelines. The first stage achieves a search space reduction for a subsequent defect type classification of over 90% with a risk of missing a defect patch of less than 10%. The second stage identifies the correct defect type to a potentially unhealthy surface area with a probability of 85%. A prototypical implementation serves as a proof of concept. This work closes the gap between requirements arising from established inspection guidelines, the demand for holistic data models which has recently become known as "digital twin", and methods for automatically identifying and measuring specific defect classes on small scale images. It is of great significance for bridge inspectors, bridge owners and authorities as they now have more suitable data models at hand to store, view and manage maintenance information on bridges including defect location and defect types which are being retrieved automatically. With these developments, a foundation is available for a complete revision of bridge inspection processes on a modern, digital basis.

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Malik, Shoaib Ahmad. „Damage detection using self-sensing composites“. Thesis, University of Birmingham, 2011. http://etheses.bham.ac.uk//id/eprint/1750/.

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The primary aim of this research programme was to enable damage detection in glass fibre reinforced composites using the reinforcing fibres as the sensing element. In other words, E-glass fibres were used as light guides to detect the fracture of individual fibres, when loaded in tension. This was achieved by monitoring the transmitted light intensity through the reinforcing glass fibres. Two types of glass fibres and matrices were evaluated. In the case of glass fibres, E-glass and custom-made small-diameter (12 µm) optical fibre (SDOF) were used. Three types of low refractive index resin systems with specified failure strains were also used. The basic technology involved illuminating one end of the fibre bundle or composite with a white light or laser source and the opposite end was imaged using a high-speed CCD camera. Acoustic emission monitoring of fibre bundles revealed that there were two types of failures occurring in a bundle, a lower amplitude of the acoustic emission signal (AES) related to the inter-fibre friction and a high amplitude of the AES to fibre fractures. This characteristic was also confirmed by a Weibull statistical analysis where it was demonstrated that a two parameter distribution was present corresponding to two different flaw distributions. In the case of self-sensing composites, it was found that the specific failure modes in the composites (matrix failure, fibre fracture, debonding) generate their characteristic amplitudes of the AES and frequencies. These failure modes were recorded and correlated to the tensile test data. It was demonstrated that the attenuation of transmitted light can be related to the fracture of fibres in the bundle or a composite test specimen. It was found that the image analysis routines were capable of identifying and tracking the survival or fracture of each fibre in the bundle or composite. The results obtained from mechanical loading, acoustic emission and images analysis were cross-correlated.

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Asnaashari, Erfan. „Vibration-based damage detection in structures“. Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/vibrationbased-damage-detection-in-structures(09061582-55fb-4fba-846e-2156dd4ef172).html.

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Structural health monitoring systems have a great potential for cost saving and safety improvement in different types of structures. One of the most important tasks of these systems is to identify damage at an early stage of its development. A variety of methods may be used to identify, locate, or quantify the extent of damage or fault in a structural or mechanical component. However, the preferable method is the one which maximises the probability of detecting the flaw, while also considering feasibility of in-situ testing, ease of use and economic factors. Cracks are one of the common defects in structural components that may ultimately lead to failure of structures if not detected. The presence of cracks in a structure brings about local variations in the stiffness of the structure. These variations cause the dynamic behaviour of the cracked structure to be different from that of a healthy one. Vibration-based damage detection methods have attracted considerable attention over the past few decades. These methods generally use changes to the physical properties of structures for the purpose of crack detection. In this thesis, two new vibration-based methods have been developed for damage detection in beam-like and rotor-type structures. The first method performs the entire signal processing required for crack detection in time domain. It is based on assessing the normality of vibration responses using the normal probability plot (NPP). The amount of deviation between the actual and normal distribution of measured vibration responses was calculated along the length of the structure to localise the crack. The second proposed method converts the vibration responses into frequency domain for further processing. Excitation of the cracked structure at a given frequency always generates higher harmonic components of the exciting frequency due to the breathing of the crack. This method uses the operational deflection shape of the structure at the exciting frequency and its higher harmonics to identify the crack location. Avoiding complicated signal processing in frequency domain is the main advantage of the first method. However, more precise identification of crack locations can be obtained through the second method. Generally, both methods have the advantage of being easy, reference-free and applicable to in-situ testing for any structure. The concept and computational approach of both methods along with their validations through numerical and experimental examples have been presented. Moreover, different input excitations have been used to evaluate the capability of the developed methods in detecting the crack location(s).

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Tadros, Nader Nabil Aziz. „Structural damage detection using ambient vibrations“. Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/18178.

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Master of Science
Department of Civil Engineering
Hani G. Melhem
The objective of this research is to use structure ambient random vibration response to detect damage level and location. The use of ambient vibration is advantageous because excitation is caused by service conditions such as normal vehicle traffic on a highway bridge, train passage on a railroad bridge, or wind loads on a tall building. This eliminates the need to apply a special impact or dynamic load, or interrupt traffic on a bridge in regular service. This research developed an approach in which free vibration of a structure is extracted from the response of this structure to a random excitation in the time domain (acceleration versus time) by averaging out the random component of the response. The result is the free vibration that includes all modes based on the sampling rate on time. Then this free vibration is transferred to the frequency domain using a Fast Fourier Transform (FFT). Variations in frequency response are a function of structural stiffness and member end-conditions. Such variations are used as a measure to identify the change in the structural dynamic properties, and ultimately detect damage. A physical model consisting of a 20 × 20 × 1670 -mm long steel square tube was used to validate this approach. The beam was tested under difference supports conditions varying from a single- to three-span continuous configuration. Random excitation was applied to the beam, and the dynamic response was measured by an accelerometer placed at various locations on the span. A numerical model was constructed in ABAQUS and the dynamic response was obtained from the finite element model subjected to similar excitation as in the physical model. Numerical results were correlated against results from the physical model, and comparison was made between the different span/support configurations. A subsequent step would be to induce damage that simulates loss of stiffness or cracking condition of the beam cross section, and that would be reflected as a change in the frequency and other dynamic properties of the structure. The approach achieved good results for a structure with a limited number of degrees of freedom. Further research is needed for structures with a larger number of degrees of freedom and structures with damage in symmetrical locations relative to the accelerometer position.

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Dixit, Akash. „Damage modeling and damage detection for structures using a perturbation method“. Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43575.

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This thesis is about using structural-dynamics based methods to address the existing challenges in the field of Structural Health Monitoring (SHM). Particularly, new structural-dynamics based methods are presented, to model areas of damage, to do damage diagnosis and to estimate and predict the sensitivity of structural vibration properties like natural frequencies to the presence of damage. Towards these objectives, a general analytical procedure, which yields nth-order expressions governing mode shapes and natural frequencies and for damaged elastic structures such as rods, beams, plates and shells of any shape is presented. Features of the procedure include the following: 1. Rather than modeling the damage as a fictitious elastic element or localized or global change in constitutive properties, it is modeled in a mathematically rigorous manner as a geometric discontinuity. 2. The inertia effect (kinetic energy), which, unlike the stiffness effect (strain energy), of the damage has been neglected by researchers, is included in it. 3. The framework is generic and is applicable to wide variety of engineering structures of different shapes with arbitrary boundary conditions which constitute self adjoint systems and also to a wide variety of damage profiles and even multiple areas of damage. To illustrate the ability of the procedure to effectively model the damage, it is applied to beams using Euler-Bernoulli and Timoshenko theories and to plates using Kirchhoff's theory, supported on different types of boundary conditions. Analytical results are compared with experiments using piezoelectric actuators and non-contact Laser-Doppler Vibrometer sensors. Next, the step of damage diagnosis is approached. Damage diagnosis is done using two methodologies. One, the modes and natural frequencies that are determined are used to formulate analytical expressions for a strain energy based damage index. Two, a new damage detection parameter are identified. Assuming the damaged structure to be a linear system, the response is expressed as the summation of the responses of the corresponding undamaged structure and the response (negative response) of the damage alone. If the second part of the response is isolated, it forms what can be regarded as the damage signature. The damage signature gives a clear indication of the damage. In this thesis, the existence of the damage signature is investigated when the damaged structure is excited at one of its natural frequencies and therefore it is called ``partial mode contribution". The second damage detection method is based on this new physical parameter as determined using the partial mode contribution. The physical reasoning is verified analytically, thereupon it is verified using finite element models and experiments. The limits of damage size that can be determined using the method are also investigated. There is no requirement of having a baseline data with this damage detection method. Since the partial mode contribution is a local parameter, it is thus very sensitive to the presence of damage. The parameter is also shown to be not affected by noise in the detection ambience.

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Preisler, Andreas [Verfasser]. „Efficient Damage Detection and Assessment Based on Structural Damage Indicators / Andreas Preisler“. Düren : Shaker, 2020. http://d-nb.info/1205239669/34.

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12

Aydogan, Mustafa Ozgur. „Damage Detection In Structures Using Vibration Measurements“. Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1058809/index.pdf.

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Cracks often exist in structural members that are exposed to repeated loading, which will certainly lower the structural integrity. A crack on a structural member introduces a local flexibility which is a function of the crack depth and location. This may cause nonlinear dynamic response of the structure. In this thesis, a new method is suggested to detect and locate a crack in a structural component. The method is based on the fact that nonlinear response of a structure with a crack will be a function of the crack location and crack magnitude. The method suggested is the extension of a recently developed technique for identification of non-linearity in vibrating multi degree of freedom system. In this method, experimentally measured receptances at different forcing levels are used as input, and the existence and location of a nonlinearity are sought. In order to validate the method, simulated experimental data is used. Characteristics of a cracked beam are simulated by using experimentally obtained analytical expressions, given in the literature. The structure itself is modelled by using finite element method. Several case studies are performed to test and demonstrate the applicability, efficiency and sensitivity of the method suggested. The effect of crack depth on nonlinear system response is also studied in numerical examples.

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Yanilmaz, Huseyin. „Damage Detection In Beams By Wavelet Analysis“. Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12609162/index.pdf.

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In this thesis, a method proposed by Han et al. [40] for detecting and locating damage in a structural member was adapted. The method was based on the energies that were calculated from the CWT coefficients of vibrational response of a cantilever beam. A transverse cut at varying depths was introduced. The presence and location of crack was investigated by processing experimentally acquired acceleration signals. Results of modal analysis and wavelet analysis of the beam with different cut depths were compared. In addition, effect of using different mother wavelets in CWT analysis for damage detection capability was investigated. Acceleration data were analyzed through CWT at different scales and CWT coefficients were calculated. Those CWT coefficients obtained from different scales were evaluated from the standpoint of damage detection. Effectiveness of energy indices associated with CWT coefficients in damage detection was demonstrated as independent of the type of mother wavelet.

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Bearzotti, Riccardo. „Structural damage detection using deep learning networks“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Research on damage detection of structures using image process- ing techniques has been actively conducted, specially on infrastruc- tures as road pavements, achieving considerably high detection accu- racies. These techniques are more and more studied all over the world cause seems be a powerful method able to replace, in some conditions, the experience and the visual ability of humans. This thesis has the purpose to introduce how the development in the last few years of the image processing can be useful to avoid some costs on structure monitoring and predict some disaster, that the most of times we listened call them as announced disasters that could be avoided. This thesis introduce the deep learning method implemented on Mat- lab to solve this problems trying to understand, in the first part, what machine learning and deep learning consist of, which is the best way to use the convolution neural networks and in which parameters work on. This we the purpose to give some background about this tech- nique in order to implement it on a large number of problems. There will be also some examples of basic codes and the outcomes are discussed, in order to figure out which is the best tool or combi- nation of tool to solve a problem of more complexity. At the end there are some consideration about useful future works that can be studied in order to help in structure monitoring in lab tests, during the life cycle and in case of collapse.

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Dincal, Selcuk. „Structural damage detection using frequency response functions“. Thesis, Texas A&M University, 2005. http://hdl.handle.net/1969.1/3129.

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This research investigates the performance of an existing structural damage detection method (SDIM) when only experimentally-obtained measurement information can be used to calculate the frequency response functions used to detect damage. The development of a SDIM that can accurately identify damage while processing measurements containing realistic noise levels and overcoming experimental modeling errors would provide a robust method for identifying damage in the larger, more complex structures found in practice. The existing SDIM program, GaDamDet, uses an advanced genetic algorithm, along with a two-dimensional finite element model of the structure, to identify the location and the severity of damage using the linear vibration information contained in frequency response functions (FRF) as response signatures. Datagen is a Matlab program that simulates the three-dimensional dynamic response of the four-story, two-bay by two-bay UBC test structure built at the University of British Columbia. The dynamic response of the structure can be obtained for a range of preset damage cases or for any user-defined damage case. Datagen can be used to provide the FRF measurement information for the three-dimensional test structure. Therefore, using the FRF measurements obtained from the UBC test structure allows for a more realistic evaluation of the performance of the SDIM provided by GaDamDet as the impact on performance of more realistic noise and model errors can be investigated. Previous studies evaluated the performance of the SDIM using only simulated FRF measurements obtained from a two-dimensional structural model. In addition, the disparity between the two-dimensional model used by the SDIM used to identify damage and the measurements obtained from the three-dimensional test structure is analyzed. The research results indicate that the SDIM is able to accurately detect structural damage to individually damaged members or to within a damaged floor, with few false damages identified. The SDIM provides an easy to use, visual, and accurate algorithm and its performance compares favorably to performance of the various damage detection algorithms that have been proposed by researchers to detect damage in the three-dimensional structural benchmark problem.

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Mustapha, Faizal. „Damage detection and localisation using novelty indices“. Thesis, University of Sheffield, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434514.

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Gerbo, Evan Jamison. „Structural Damage Detection Utilizing Experimental Mode Shapes“. DigitalCommons@CalPoly, 2014. https://digitalcommons.calpoly.edu/theses/1247.

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A method of locating structural damage is developed and tested to aid in the evaluation of structural health. This method will help minimize the cost of structural inspection and repair by informing engineers of where damage, due a seismic event, has occurred before the removal of finishes for visual inspection. This thesis begins to answer the question “can structural damage be detected solely through analysis of experimentally measured mode shapes?” The work encompasses construction of a test structure, with three braces that can be repeatedly engaged or dis-engaged, thus allowing for testing of a variety of braced configurations. For this thesis, damage is assumed to cause a change in stiffness. Experimental testing is conducted to acquire mode shapes and frequencies for the 6 dominant modes of the test structure. Lastly, the data is analyzed to identify the configuration of braces engaged on the structure. The accuracy of the method is assessed by the number of configurations that it correctly predicts and the confidence of the predictions.

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Villani, Luis Gustavo Giacon. „Robust damage detection in uncertain nonlinear systems /“. Ilha Solteira, 2019. http://hdl.handle.net/11449/191200.

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Orientador: Samuel da Silva
Abstract: Structural Health Monitoring (SHM) methodologies aim to develop techniques able to detect, localize, quantify and predict the progress of damages in civil, aerospatial and mechanical structures. In the hierarchical process, the damage detection is the first and most important step. Despite the existence of numerous methods of damage detection based on vibration signals, two main problems can complicate the application of classical approaches: the nonlinear phenomena and the uncertainties. This thesis demonstrates the importance of the use of a stochastic nonlinear model in the damage detection problem considering the intrinsically nonlinear behavior of mechanical structures and the measured data variation. A new stochastic version of the Volterra series combined with random Kautz functions is proposed to predict the behavior of nonlinear systems, considering the presence of uncertainties. The stochastic model proposed is used in the damage detection process based on hypothesis tests. Firstly, the method is applied in a simulated study assuming a random Duffing oscillator exposed to the presence of a breathing crack modeled as a bilinear oscillator. Then, an experimental application considering a nonlinear beam subjected to the presence of damage with linear characteristics (loss of mass in a bolted connection) is performed, with the direct comparison between the results obtained using a deterministic and a stochastic model. Finally, an experimental application considering a n... (Complete abstract click electronic access below)
Resumo: As metodologias de Monitoramento da Integridade Estrutural (SHM) visam desenvolver técnicas capazes de detectar, localizar, quantificar e prever o progresso de danos em estruturas civis, aeroespaciais e mecânicas. Nesse processo hierárquico, a detecção de danos é o primeiro e mais importante passo. Apesar da existência de inúmeros métodos de detecção de danos baseados em sinais de vibração, dois problemas principais podem complicar a aplicação de abordagens clássicas: os fenômenos não lineares e as incertezas. Esta tese demonstra a importância do uso de um modelo não linear estocástico no problema de detecção de danos, considerando o comportamento intrinsecamente não linear de estruturas mecânicas e a variação dos dados medidos. Uma nova versão estocástica das séries de Volterra, combinada com funções aleatórias de Kautz, é proposta para prever o comportamento de sistemas não lineares, considerando a presença de incertezas. O modelo estocástico proposto é utilizado no processo de detecção de danos com base em testes de hipótese. Primeiramente, o método é aplicado em um estudo simulado, assumindo um oscilador Duffing aleatório exposto à presença de uma trinca respiratória modelada como um oscilador bilinear. Em seguida, uma aplicação experimental é realizada considerando uma viga não linear sujeita à presença de um dano com características lineares (perda de massa em uma conexão parafusada), com a comparação direta entre os resultados obtidos utilizando um modelo determinístic... (Resumo completo, clicar acesso eletrônico abaixo)
Doutor

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Mejia, Paloma Yasmin. „Smart Systems for Damage Detection and Prognosis“. Miami University Honors Theses / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=muhonors1114101552.

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20

Kim, Daewon. „Phased Array Damage Detection and Damage Classification in Guided Wave Structural Health Monitoring“. Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77073.

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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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Campbell, Marvin G. „Structural damage detection using frequency domain error localization“. Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA289932.

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22

Kannappan, Laxmikant Aerospace Civil &amp Mechanical Engineering Australian Defence Force Academy UNSW. „Damage detection in structures using natural frequency measurements“. Awarded by:University of New South Wales - Australian Defence Force Academy. Aerospace, Civil & Mechanical Engineering, 2009. http://handle.unsw.edu.au/1959.4/44852.

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In the last two decades, the emphasis in aircraft maintenance has been on developing online structural health monitoring systems to replace conventional non destructive inspection techniques which require considerable down-time, human effort and cost. Vibration based damage detection is one of the most promising techniques for implementation in Structural Health Monitoring (SHM). In vibration based methods, the presence of damage is detected by monitoring changes in one of the dynamic parameters of the structure, resonant frequencies, modeshapes or damping characteristics. Compared to modeshape based methods, frequency based methods have the advantage that measurements need to be taken only at a single location. Previous developments on frequency based techniques have relied on Finite Element Model updating; analytical techniques have hitherto been restricted to beams due to the complexity in developing equations for cracked two dimensional structures. In this thesis the analytical approach using an energy formulation is extended to plates with through-thickness cracks, where modeshapes from either numerical modelling or experimental measurements can be employed to determine the energy of vibration. It is demonstrated that by using a hybrid approach, incorporating experimentally measured modeshapes along with measured changes in frequencies, the damage parameters can be estimated without resorting to theoretical modelling or numerical analysis. The inverse problem of finding the crack location, size and orientation from measured changes in frequencies is addressed using minimisation techniques. The forward problem and the inverse algorithm is first validated using numerical simulation and experimental testing of beams with edge cracks and centre cracks. The application of the methodology to the two dimensional case is then validated by numerical simulation and experimental modal analysis of plates with through thickness cracks. A statistical procedure is developed for determination of the 90/95 probability of crack detection and the minimum detectable crack size in both cases. It is demonstrated that the measurement of frequency changes can be successfully employed to detect and assess the location and size of cracks in beams and plates, using modeshapes from theory, Finite Element Analysis.

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Kumar, Yadav Susheel. „Damage Detection and Characterization in Plate Like Structures“. Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/306997.

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Large civil infrastructure systems all over the world have become an integral part of our civilization. The inspection and maintenance of these structures for public safety is a difficult task. The assessment of integrity of such huge structures due to local damages is even more difficult to deal with. The conventional inspections are performed manually, generally by visual examination and sometimes by more advanced techniques like ultrasonic, electromagnetic and fiber optic techniques. These inspections involve human interventions, depend on individual inspector's experience, and are time consuming. Such inspection methods may not be very useful for real time health assessment of a structure in service and as a result are not very helpful in preventing any disastrous situation through early warning. Therefore, it is very important to look for a comprehensive strategy of global integrity monitoring infused with information about local damages in the structure. For local damage assessment the current state of the health monitoring technology lacks a generalized and definitive approach to the identification and localization of damage. In past decades several signal processing tools have been used for solving different health monitoring problems but the commutability of the tools between different problems has been restricted. Fundamental reasons for this shortcoming have never been investigated in detail. In this dissertation an investigation has been carried out employing almost all promising feature extraction tools on a representative problem - a plate with rivet holes. The problem considered has radial cracks around rivet holes in a joint panel of a steel truss bridge. Such defects are very difficult to detect. Although well established, Lamb wave based nondestructive evaluation techniques are revisited and new tools are developed to address this issue. Simulation of the scattered ultrasonic wave field is carried out using the finite element method. This ultrasonic wave field is further analyzed to evaluate the integrity of the structure using various feature extraction (FE) techniques. Joint time-frequency-energy representation is obtained from ultrasonic signals recorded at various locations on the plate (joint panel) and used to extract damage sensitive features. Those features were then used to formulate a new Damage Parameter (DP) for better visualization of the crack. Results are shown to demonstrate the comparative effectiveness of these techniques. It is concluded that any particular FE technique cannot detect all possible sizes and orientations of the crack. It is suggested that the statistical occurrence and pattern of the crack must be visualized through a few selective FE techniques in a sequence. Modeling of the wave scattering phenomenon by conventional numerical techniques such as finite element method requires very fine mesh at high frequencies necessitating heavy computational power. Distributed point source method (DPSM) which is a recently developed semi-analytical technique, is applied to model the scattering of ultrasonic wave field on representative problem geometries and the results are used to diagnose structural damages. DPSM is a newly developed robust mesh-free technique for simulating ultrasonic, electrostatic and electromagnetic field problems. In most of the previous studies the DPSM technique has been applied to model two dimensional surface geometries and relatively simple three dimensional scatterer geometries. It has been very difficult to perform the wave scattering analysis for very complex three-dimensional geometries. This technique has been extended to model wave scattering in an arbitrary geometry. The simulation has been carried out with and without the presence of cracks near the rivet holes.

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Amraoui, Mohamed Yacine. „Non-invasive damage detection and structural health monitoring“. Thesis, University of Bristol, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271865.

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Qiao, Long. „Structural damage detection using signal-based pattern recognition“. Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1385.

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Lertpaitoonpan, Wirat. „Bridge damage detection using a system identification method“. [Florida] : State University System of Florida, 2000. http://etd.fcla.edu/etd/uf/2000/amt2446/WiratDissertation3-10-00.pdf.

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Thesis (Ph. D.)--University of Florida, 2000.
Title from first page of PDF file. Document formatted into pages; contains xvi, 155 p.; also contains graphics. Vita. Includes bibliographical references (p. 152-154).

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Sawatzky, Rene. „Vibration Based Planetary Gear Analysis and Damage Detection“. DigitalCommons@CalPoly, 2014. https://digitalcommons.calpoly.edu/theses/1378.

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With this thesis the research at the engineering department is continued, based on previous thesis projects. These projects were considering the possibility of simulation gears with rigid bodies. After researches with different emphasizes on the rigid body simulation, the gained knowledge showed limitations of the rigid body model. Gear failures are very diverse and the actual simulation technique could not represent all necessary failure modes that can occur. That led to this thesis as a research project to find a way to detect and analyze the failure modes that cannot be considered with the current rigid body approach. With the flexible body theory and simulation tools (MSC:Adams) this gap of gear failure detection simulation can be closed. The objective is, making it possible to simulate all failure modes of a gear that can occur. Additionally the previous project on this topic were using gears of small laboratory conditions. An industry sector has been picked to work on a practical application. This application is a wind turbine gearbox. These gearboxes have common run-time errors which influences the profitability of the power generation. To model this system a gear design guide for wind turbine gearboxes is elaborated.

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Davis, Ivan Christopher. „Damage Detection in Aluminum Cylinders Using Modal Analysis“. Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/34317.

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Many studies have attempted to detect structural damage by examining differences in the frequency response functions of a structure before and after damage. In an experimental setting, this variation can not be attributed solely to the addition of damage. Other sources of variation include testing and structure variation. Examples of testing variation include the error introduced by modal parameter extraction, measurement noise, and the mass loading of the accelerometer. Structure variability is due to slight differences in the supposedly identical structures. Dimensional tolerancing is one example. This study began with six "identical" undamaged aluminum cylinders, of which three were later damaged to varying extents. The frequency response functions of the undamaged and damaged cylinders were measured. Also, the frequency response function of the same undamaged cylinder was measured multiple times to investigate testing variation. The contributions of testing, cylinder, and damage variation to the differences between cylinder responses was elucidated by specifically examining their frequency response functions in two ways: comparing the natural frequencies and directly investigating the entire frequency response function. The curvature of the frequency response functions was then used to determined the presence, location, and severity of the imparted damage.
Master of Science

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Lu, Kan. „Dynamics Based Damage Detection of Plate-Type Structures“. University of Akron / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=akron1133818717.

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Liu, Ning. „Composite materials impact damage detection using neural networks“. Thesis, Aston University, 2002. http://publications.aston.ac.uk/11838/.

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This thesis considers two basic aspects of impact damage in composite materials, namely damage severity discrimination and impact damage location by using Acoustic Emissions (AE) and Artificial Neural Networks (ANNs). The experimental work embodies a study of such factors as the application of AE as Non-destructive Damage Testing (NDT), and the evaluation of ANNs modelling. ANNs, however, played an important role in modelling implementation. In the first aspect of the study, different impact energies were used to produce different level of damage in two composite materials (T300/914 and T800/5245). The impacts were detected by their acoustic emissions (AE). The AE waveform signals were analysed and modelled using a Back Propagation (BP) neural network model. The Mean Square Error (MSE) from the output was then used as a damage indicator in the damage severity discrimination study. To evaluate the ANN model, a comparison was made of the correlation coefficients of different parameters, such as MSE, AE energy, AE counts, etc. MSE produced an outstanding result based on the best performance of correlation. In the second aspect, a new artificial neural network model was developed to provide impact damage location on a quasi-isotropic composite panel. It was successfully trained to locate impact sites by correlating the relationship between arriving time differences of AE signals at transducers located on the panel and the impact site coordinates. The performance of the ANN model, which was evaluated by calculating the distance deviation between model output and real location coordinates, supports the application of ANN as an impact damage location identifier. In the study, the accuracy of location prediction decreased when approaching the central area of the panel. Further investigation indicated that this is due to the small arrival time differences, which defect the performance of ANN prediction. This research suggested increasing the number of processing neurons in the ANNs as a practical solution.

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Murugesan, Kaviraj. „Damage detection on railway bridges using system identification“. Thesis, Karlstads universitet, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-28595.

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Rule, Ruth Anne. „Vibration-based damage detection in ceramics and glass“. Thesis, Loughborough University, 2000. https://dspace.lboro.ac.uk/2134/27153.

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This thesis describes the development of the hardware, experimental procedures and algorithms required for vibration based damage identification in small ceramic and glass structures. The results form the basis of a fully automated industrial quality assurance system.

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Lucas, Lynda T. „Detection of DNA damage caused by N-nitrosoindoles“. Thesis, University of Leicester, 2001. http://hdl.handle.net/2381/30755.

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To evaluate the genotoxicity of N-nitrosoindoles, three model compounds, 1-nitrosoindole-3-acetonitrile (NIAN), 1-nitrosoindole-3-acetamide and l-nitrosoindole-3-acetic acid methyl ester, were reacted with isolated purine nucleotides at physiological pH. The profile of reaction products was identical for each of the N-nitrosoindoles. The results indicated that N-nitrosoindoles can efficiently transfer the nitroso group to nucleophilic targets in isolated purine nucleotides, causing depurination, deamination coupled with depurination to afford hypoxanthine and xanthine, and formation of a novel deoxyguanosine monophosphate analogue, 2'-deoxyoxanosine monophosphate and its corresponding depurination product, oxanine. These pathways of modification were preserved at the macromolecular level in oligonucleotides and calf thymus DNA, with guanine residues appearing to be a primary site of reaction. The studies revealed an additional cross-linked product at CG residues in NIAN-treated duplex DNA. Pyrimidine residues were inactive toward nitroso transfer by NIAN. The ability of the nitroso group to exert damage at the nuclei was demonstrated in vivo in the glandular stomach of CD-I mice via detection of abasic site damage, and in single cells in vitro as shown by the Comet assay. NIAN was mutagenic in the Ames II assay. In contrast to many other genotoxic N-nitrosocompounds, which are known to alkylate DNA, the genotoxicity of N-nitrosoindoles arises via efficient transnitrosation to nucleophilic sites on the purine bases. All of the products resulting from transnitrosation by N-nitrosoindoles are potentially mutagenic if they occur in vivo.

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Acharya, Dabit. „COMPARATIVE EXPERIMENTAL STUDIES FOR GLOBAL DAMAGE DETECTION IN PLATES USING THE SCANNING LASER VIBROMETER TECHNIQUES“. University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1155079600.

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Gul, Mustafa. „INVESTIGATION OF DAMAGE DETECTION METHODOLOGIES FOR STRUCTURAL HEALTH MONITORING“. Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3317.

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Structural Health Monitoring (SHM) is employed to track and evaluate damage and deterioration during regular operation as well as after extreme events for aerospace, mechanical and civil structures. A complete SHM system incorporates performance metrics, sensing, signal processing, data analysis, transmission and management for decision-making purposes. Damage detection in the context of SHM can be successful by employing a collection of robust and practical damage detection methodologies that can be used to identify, locate and quantify damage or, in general terms, changes in observable behavior. In this study, different damage detection methods are investigated for global condition assessment of structures. First, different parametric and non-parametric approaches are re-visited and further improved for damage detection using vibration data. Modal flexibility, modal curvature and un-scaled flexibility based on the dynamic properties that are obtained using Complex Mode Indicator Function (CMIF) are used as parametric damage features. Second, statistical pattern recognition approaches using time series modeling in conjunction with outlier detection are investigated as a non-parametric damage detection technique. Third, a novel methodology using ARX models (Auto-Regressive models with eXogenous output) is proposed for damage identification. By using this new methodology, it is shown that damage can be detected, located and quantified without the need of external loading information. Next, laboratory studies are conducted on different test structures with a number of different damage scenarios for the evaluation of the techniques in a comparative fashion. Finally, application of the methodologies to real life data is also presented along with the capabilities and limitations of each approach in light of analysis results of the laboratory and real life data.
Ph.D.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering PhD

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Camacho, Navarro Jhonatan. „Robust structural damage detection by using statistical hybrid algorithms“. Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/667239.

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This thesis presents the results of applying a statistical hybrid approach for structural health monitoring using piezo actuating signals. Where, by combining statistical processing based on Principal Component Analysis (PCA), cross-correlation functions and pattern recognition methods it was possible to detect, classify and locate damages under varying environmental conditions and possible sensor faults. The proposed methodology consists of first transmiting/sensing guided waves along the monitored structure surface by using piezoelectric (PZT) devices. Then, cross-correlated piezoelectric signals are statistically represented by means of a PCA model. Later, damages are identified through error indexes computed from a statistical baseline model. Finally, clustering methods and scattered plots are used to verify the performance of the proposed algorithm. Improved or new techniques are presented in this thesis which were focused to achieve more reliable diagnosis with high robustness and good performance. Specifically, differential genetic algorithms are used for automatically tuning parameters in a PCA-SOM damage detection/classification approach. Additionally, Ensemble Learning is explored as approach for obtaining more efficient diagnosis with high separable boundaries between undamaged and damage conditions taking advantages of learner algorithms built from Non-Linear PCA and a Multiactuacting active scheme of piezodiagnostics. Also, a modified version of the Reconstruction Algorithm for Probabilistic Inspection of Damage – RAPID is implemented to solve location tasks in SHM. The proposed methodology was experimentally evaluated on different structures such a a carbon-steel pipe loop, a laminate plate, aircraft wings and a scale tower wind, among others; where different damage scenarios were studied, including leaks scenarios, mass adding and cuts. The effectiveness of the proposed methodology to detect, locate and classify damages under varying environmental and operational conditions is demonstrated. Likewise, the feasibility for continuous monitoring is validated by embedding the code of the proposed algorithm whose capacity to detect structural damages was demonstrated. As a result, the combination of piezodiagnostics approach, cross-correlation analysis, principal component analysis, clustering techniques and Ensemble Learning become as promising solution in the field of structural health monitoring and specifically to achieve a robust solution for damage detection and location.
Esta tesis presenta los resultados de la aplicación de un enfoque híbrido estadístico para el monitoreo de salud estructural utilizando señales piezoeléctrica. Donde, al combinar procesamiento estadístico basado en análisis de componentes principales (PCA), funciones de correlación cruzada y métodos de reconocimiento de patrones fue posible detectar, clasificar y localizar daños en diferentes condiciones ambientales y posibles fallas en los sensores. La metodología desarrollada consiste en primero transmitir ondas guiadas a lo largo de la superficie de la estructura monitorizada mediante el uso de dispositivos piezoeléctricos (PZT). Luego, las señales de correlación cruzada calculadas sobre las mediciones piezoeléctricas se representan estadísticamente por medio de un modelo de línea base obtenido mediante PCA. Posteriormente, los daños se identifican mediante índices de error calculados a partir del modelo estadístico de referencia. Finalmente, se utilizan métodos de aprendizaje no supervisado y gráficos de dispersión para verificar el rendimiento del algoritmo propuesto. En esta tesis se presentan nuevas técnicas o versiones mejoradas para lograr un diagnóstico más confiable con alta robustez y buen rendimiento. Específicamente, se utilizan algoritmos genéticos diferenciales para ajustar automáticamente los parámetros en un algoritmo de clasificación y detección de daños basado en PCA y Mapas auto-organizados (SOM). Además, se analiza Ensemble Learning como un enfoque para obtener un diagnóstico más eficiente con mejores fronteras de separación entre condiciones con y sin daño, combinando diferentes algoritmos de aprendizaje construidos a partir de PCA no lineal y lineal así como un esquema activo de multiactuación de piezodiagnóstico. Adicionalmente, se implementa una versión modificada del algoritmo de reconstrucción para la inspección probabilística de daños (RAPID) para estimar la localización del daño. La metodología propuesta se validó experimentalmente en diferentes estructuras, como un circuito de tubería de acero al carbono, una placa laminada, alas de avión y un generador de viento a escala, entre otros; donde se estudiaron diferentes escenarios de daños, incluidos escenarios de fugas, agregación de masa y grietas. Se demuestra la efectividad de la metodología propuesta para detectar, localizar y clasificar daños en diferentes condiciones ambientales y operativas. Del mismo modo, la viabilidad del monitoreo continuo se valida implementando el código del algoritmo propuesto en un sistema embebido, cuya capacidad para detectar daños estructurales se demostró. Como resultado, la combinación del enfoque de piezodiagnóstico, análisis de correlación cruzada, análisis de componentes principales, técnicas de aprendizaje no supervisado y Ensemble Learning se obtiene una solución prometedora en el campo del monitoreo de la salud estructural y específicamente para lograr una solución robusta para la detección de daños y la ubicación.

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Ge, Ma. „Structural damage detection and identification using system dynamic parameters“. Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2005. http://wwwlib.umi.com/cr/syr/main.

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Brett, Peter T. B. „Urban damage detection in high resolution amplitude SAR images“. Thesis, University of Surrey, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604334.

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The majority of the world's human population lives in towns and cities. High population densities mean that damage and disruption caused by natural disasters and other events have a much greater impact when urban areas are affected. Satellite remote sensing has the potential to play an important role in urban disaster monitoring and management, thanks to its relative immunity to disruption by terrestrial events. When data is required promptly and at short notice, Synthetic Aperture Radar (SAR) is of particular interest because of its ability to penetrate atmospheric conditions such as cloud and smoke. This thesis describes some recent contributions in the field of urban SAR, and in particular the exploitation of SAR amplitude images from metre-resolution satellite SAR systems such as TerraSAR-X and COSMO/SkyMed. An efficient curvilinear feature detection algorithm based on the Lindeberg scale-space ridge detector is introduced, and used for extraction of bright lines from SAR images. Two novel feature classification techniques for the detection of buildings are described, based on model selection: one supervised approach using local brightness and ridge strength statistics of bright line points, and one unsupervised approach using shape-dependent statistics of curvilinear features and priors derived from idealised building geometry. Finally, this thesis discusses the successful integration of these methods into an unsupervised tool for earthquake damage detection. The effectiveness of the models, algorithms and software tools is demonstrated and illustrated using SAR data from the COSMO/SkyMed constellation covering the 2009 earthquake in L' Aquila, Italy and the 2010 earthquake in Port-au-Prince, Haiti. The research described in this thesis provides the conceptual basis for a whole new approach to urban feature extraction, classification and change detection, using statistical models for the actual geometry of urban structure

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Dutta, Debaditya. „Ultrasonic Techniques for Baseline-Free Damage Detection in Structures“. Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/3.

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This research presents ultrasonic techniques for baseline-free damage detection in structures in the context of structural health monitoring (SHM). Conventional SHM methods compare signals obtained from the pristine condition of a structure (baseline signals) with those from the current state, and relate certain changes in the signal characteristics to damage. While this approach has been successful in the laboratory, there are certain drawbacks of depending on baseline signals in real field applications. Data from the pristine condition are not available for most existing structures. Even if they are available, operational and environmental variations tend to mask the effect of damage on the signal characteristics. Most important, baseline measurements may become meaningless while assessing the condition of a structure after an extreme event such as an earthquake or a hurricane. Such events may destroy the sensors themselves and require installation of new sensors at different locations on the structure. Baselinefree structural damage detection can broaden the scope of SHM in the scenarios described above. A detailed discussion on the philosophy of baseline-free damage detection is provided in Chapter 1. Following this discussion, the research questions are formulated. The organization of this document and the major contributions of this research are also listed in this chapter. Chapter 2 describes a fully automated baseline-free technique for notch and crack detection in plates using a collocated pair of piezoelectric wafer transducers for measuring ultrasonic signals. Signal component corresponding to the damage induced mode-converted Lamb waves is extracted by processing the originally measured ultrasonic signals. The damage index is computed as a function of this mode-converted Lamb wave signal component. An over-determined system of Lamb wave measurements is used to find a least-square estimate of the measurement errors. This error estimate serves as the damage threshold and prevents the occurrences of false alarms resulting from imperfections and noise in the measurement system. The threshold computation from only the measured signals is they key behind baseline-free damage detection in plates. Chapters 3 and 4 are concerned with nonlinear ultrasonic techniques for crack detection in metallic structures. Chapter 3 describes a nonlinear guided wave technique based on the principle of super-harmonic production due to crack induced nonlinearity. A semi-analytical method is formulated to investigate the behavior of a bilinear crack model. Upon comparing the behavior with experimental observations, it is inferred that a bilinear model can only partially capture the signal characteristics arising from a fatigue crack. A correlation between the extents of nonlinear behavior of a breathing crack with the different stages of the fatigue crack growth is also made in Chapter 3. In Chapter 4, a nonlinear system identification method through coherence measurement is proposed. A popular electro-magnetic impedance circuit was used to detect acoustic nonlinearity produced by a crack. Chapters 5 and 6 comprise the final part of this thesis where wavefield images from a scanning laser vibrometer are digitally processed to detect defects in composite structures. Once processed, the defect in the scanned surface stands out as an outlier in the background of the undamaged area. An outlier analysis algorithm is then implemented to detect and localize the damage automatically. In Chapter 5, exploratory groundwork on wavefield imaging is done by obtaining wave propagation images from specimens made of different materials and with different geometries. In Chapter 6, a hitherto unnoted phenomenon of standing wave formation in delaminated composite plates is observed and explained. Novel signal and image processing techniques are also proposed in this chapter, of which the isolation of standing waves using wavenumber-frequency domain manipulation and the use of Laplacian image filtering technique deserve special mention.

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Smit, Wynand Gerhardus. „Fan blade damage detection using on-line vibration monitoring“. Diss., Pretoria : [s.n.], 2002. http://upetd.up.ac.za/thesis/available/etd-11302005-091637/.

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41

Uwayed, Ahmed Noori. „Damage detection in laminated composite structures using dynamic analysis“. Thesis, University of Leicester, 2018. http://hdl.handle.net/2381/42921.

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Laminated composite materials are used in different applications, for example mechanical, civil and aerospace structures, due to their light weight and excellent mechanical properties. However, fibre breakage and delamination are among the more serious damage that often initiate and propagate due to a number of mechanical and, specifically, dynamic loads during the operational life. Also, these damages destroy design functionality of these structures. To address this issue, damage detection is required in time to provide a good understanding of structure state in advance of any potential failure. There are a number of damage detection approaches reported in the literature and reviewed herein. Some of these are base-line free, whilst others use the intact data as a reference for the detection of damaged sections. However, currently there are a very limited number of experimental studies in the literature that use vibration-based damage detection to detect the delaminated areas, and are almost non-existent for fibre breakage; the majority of simulated studies consider delamination only. Defects in laminated structures are quite complicated and in most cases are hidden. Frequency-based damage detection is considered to be a global approach and is not useful when dealing with complex structures. There has been extensive research to develop the curvature mode shape as a reference for damage detection because it is highly sensitive at show the effects of damage. This sensitivity is tested in this research, as it is extremely difficult to detect damaged sections within composite materials, even with an active approach. Hence, the main objective of this research is to develop the curvature damage index by calculating the irregularity curvature index, and the proposal of a novel index, called the Haar index, to support the damage detection process. Both these indexes are used to detect delamination and fibre breakage on high modulus CFRP plate structures under condition of free vibration. Using these indexes gives an efficient method by which to quantify and localize damaged areas in both theoretical and experimental considerations of different lay-ups. In the modelling section, two finite element software programs, COMSOL Multiphysics 5.1 (Licence No. 7074366) and ABAQUS 6.14-1 (Licence No. 200000000008515), are used. This thesis includes development procedure of the curvature index, calculates the Haar index, gives details of the theoretical and experimental analysis, and reports the consequent results and discussion.

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Polimeno, Umberto. „Non linear spectroscopy for damage detection on aerospace materials“. Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535639.

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Gong, Peng. „Ultrasonic Signal Processing for Structural Damage Detection and Quantification“. Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/674.

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The operation of our society depends heavily on infrastructure systems. To prevent failures and to reduce costs of maintenance, structural health monitoring (SHM) systems have been implemented on an increasing number of infrastructure systems. SHM systems have the potential to give reliable prediction of structural deterioration with less human safety risk and labor costs, and without interruption of normal operations. In the field of SHM, many techniques have been proposed in recent decades. Among these techniques, ultrasonic testing has been widely used for damage characterization in structures and materials. However, there remain many challenges in real-world SHM applications. For example, temperature variations can cause a significant decrease in performance of ultrasonic testing. Although there exist some temperature compensation techniques to improve the performance of ultrasonic testing under temperature variations, these techniques have their own limitations. This dissertation will focus on novel ultrasonic signal processing techniques for damage detection, quantification and temperature compensation. In Chapter 2, I will propose a modified optimal signal stretching (OSS) method and an singular value decomposition (SVD) method to solve the temperature compensation problem, where the OSS method (in its original form) failed to perform well for damage detection. In Chapter 3, I will study the statistical orthogonal relationship between temperature-induced and damage-induced ultrasonic change signals. The orthogonal relationship can be used to explain why SVD performs well under varying temperature conditions and why it also has the potential (under some conditions) to be directly used for damage detection and quantification. In Chapter 4, I viii will study the ultrasonic time-of-flight diffraction technique, which is used to quantify wall thickness loss of thick-walled aluminum tubes, because the conventional pulse-echo method did not perform well in my target application. In Chapter 5, I will propose a novel ultrasonic passband technique to quantify the alkali-silica reaction (ASR) caused cracking damage in concrete structures. This technique is based on the ultrasonic wave filtering effects of cracks in concrete. With the progress of ASR caused cracking damage in concrete, more high frequency components of ultrasonic waves are filtered out than low frequency components. The research work in this dissertation has the potential to help advance ultrasonic SHM techniques, to improve the real-world performance of ultrasonic SHM, to prevent failures of infrastructure systems, and to reduce the costs of maintenance if the proposed ultrasonic techniques can be implemented in real infrastructure systems in the future. However, some future work still needs to be done in order to implement the techniques studied in this dissertation in real-world applications.

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Goi, Yoshinao. „Bayesian Damage Detection for Vibration Based Bridge Health Monitoring“. Kyoto University, 2018. http://hdl.handle.net/2433/232013.

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Anderson, Matthew Francis. „Parametric investigation of strain gauges in structural damage detection“. Thesis, University of Iowa, 2013. https://ir.uiowa.edu/etd/2436.

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Vibration-based damage detection (VBDD) methods are used to detect damage in structural members non-evasively. This investigation began with two objectives: to prove a VBDD method could detect damage using strain gauges both analytically and experimentally, and to then use that method to determine the distance from a damaged area that strain gauges could be effective. Work began simultaneously using finite element software and physical experiments. It was determined that a VBDD method could detect damage with strain gauges in both settings. A parametric study was then completed that used probabilistic methods to identify an effective range for strain gauges over the length of the structural member.

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Long, James Ph D. Massachusetts Institute of Technology. „Automated structural damage detection using one class machine learning“. Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90062.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 101-103).
Measuring and analysing the vibration of structures using sensors can help identify and detect damage, potentially prolonging the life of structures and preventing disasters. Wireless sensor systems promise to make this technology more affordable and more widely applicable. Data driven structural health monitoring methodologies take raw signals obtained from sensor networks, and process them to obtain damage sensitive features. New measurements are then compared with baselines to detect damage. Because damage-sensitive features also exhibit variation due to environmental and operational changes, these comparisons are not always straightforward and sophisticated statistical analysis is necessary in order to detect abnormal changes in the damage sensitive features. In this thesis, an automated methodology which uses the one-class support vector machine (OCSVM) for damage detection and localisation is proposed. The OCSVM is a nonparametric machine learning method which can accurately classify new data points based only on data from the baseline condition of the structure. This methodology combines feature extraction, by means of autoregressive modeling, and wavelet analysis, with statistical pattern recognition using the OCSVM. The potential for embedding this damage detection methodology at the sensor level is also discussed. Efficacy is demonstrated using real experimental data from a steel frame laboratory structure, for various damage locations and scenarios.
by James Long.
S.M.

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Sharma, Utshree. „Damage Detection in a Steel Beam using Vibration Response“. Youngstown State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1596222984454508.

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Mahmood, Lutphy A. „The detection of laser-induced damage in optical materials“. Thesis, Loughborough University, 1985. https://dspace.lboro.ac.uk/2134/28137.

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Laser-induced damage caused by the interaction of intense optical radiation with matter is one of the most serious problems encountered in the operation and design of high-energy laser systems. The most widely used technique to detect damage is to simply irradiate and visually inspect the sample for signs of damage. This method although valuable does not provide real-time damage monitoring and has limited quantitative capabilities.

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Angelopoulos, Nikolaos. „Damage detection and damage evolution monitoring of composite materials for naval applications using acoustic emission testing“. Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7597/.

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Maritime transport has profound importance for the world economy. Vessels of all sizes constantly transport large numbers of passengers and goods across the sea, often under adverse operational conditions. Vessels need to exhibit high levels of reliability, availability, maintainability and safety (RAMS). However, at the same time their performance needs to be optimised ensuring the lowest possible fuel consumption with the maximum operational capacity and range without compromising RAMS. Sweating of naval assets and profitability should be maximised for the operator ensuring investment in future projects and supporting the growth of maritime transport and world economy as a whole. Vessels have been traditionally manufactured using naval steel grades such AH, DH and EH. Smaller leisure and specialised purpose vessels such as patrol boats, etc. have been built using fibre-reinforced composite (FRC) materials. This trend is gradually penetrating the market of larger commercial vessels including freight and cruise ships. However, these are still the early days and further investigation of the optimum FRC manufacturing techniques and mechanical properties together with an in-depth understanding of the damage mechanics are required before such materials can become more commonplace. This project has investigated different glass FRCs using different manufacturing techniques. Glass fibres are preferred due to their lower cost in comparison with carbon fibres. The use of carbon FRCs in maritime applications is limited to the fabrication of racing and high performance speedboat vessels. Samples manufactured under laboratory conditions have been compared with those manufactured by a shipyard. It has been seen that the in-house samples had generally superior performance. Steel-to-composite joints have also been assessed including different designs. The effect of different features in the design such as drilled holes and bolts on the mechanical performance of the manufactured samples has also been evaluated. The damage mechanisms involved during damage propagation and features causing damage initiation have been considered. Damage initiation and subsequent evolution have been monitored using acoustic emission (AE). Various signal processing approaches have been employed (manual and automatic) for optimum evaluation of the AE data obtained in a semiquantitative manner. It has been shown that AE could be applied effectively for structural health monitoring of naval structures in the field. Several factors and parameters that need to be considered during acquisition and analysis have been successfully determined. The key results of the study together with mechanical testing and characterisation of samples employed are presented in summarised form within the present thesis.

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Zemmour, Arnaud I. „The Hilbert-Huang transform for damage detection in plate structures“. College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3832.

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Thesis (M.S.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Dept. of Aerospace Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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