896 resultados para structural health monitoring (SHM)


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Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure. This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels. The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.

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This paper describes the formulation for the free vibration of joined conical-cylindrical shells with uniform thickness using the transfer of influence coefficient for identification of structural characteristics. These characteristics are importance for structural health monitoring to develop model. This method was developed based on successive transmission of dynamic influence coefficients, which were defined as the relationships between the displacement and the force vectors at arbitrary nodal circles of the system. The two edges of the shell having arbitrary boundary conditions are supported by several elastic springs with meridional/axial, circumferential, radial and rotational stiffness, respectively. The governing equations of vibration of a conical shell, including a cylindrical shell, are written as a coupled set of first order differential equations by using the transfer matrix of the shell. Once the transfer matrix of a single component has been determined, the entire structure matrix is obtained by the product of each component matrix and the joining matrix. The natural frequencies and the modes of vibration were calculated numerically for joined conical-cylindrical shells. The validity of the present method is demonstrated through simple numerical examples, and through comparison with the results of previous researchers.

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Acoustic emission (AE) analysis is one of the several diagnostic techniques available nowadays for structural health monitoring (SHM) of engineering structures. Some of its advantages over other techniques include high sensitivity to crack growth and capability of monitoring a structure in real time. The phenomenon of rapid release of energy within a material by crack initiation or growth in form of stress waves is known as acoustic emission (AE). In AE technique, these stress waves are recorded by means of suitable sensors placed on the surface of a structure. Recorded signals are subsequently analysed to gather information about the nature of the source. By enabling early detection of crack growth, AE technique helps in planning timely retrofitting or other maintenance jobs or even replacement of the structure if required. In spite of being a promising tool, some challenges do still exist behind the successful application of AE technique. Large amount of data is generated during AE testing, hence effective data analysis is necessary, especially for long term monitoring uses. Appropriate analysis of AE data for quantification of damage level is an area that has received considerable attention. Various approaches available for damage quantification for severity assessment are discussed in this paper, with special focus on civil infrastructure such as bridges. One method called improved b-value analysis is used to analyse data collected from laboratory testing.

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This research has successfully developed a novel synthetic structural health monitoring system model that is cost-effective and flexible in sensing and data acquisition; and robust in the structural safety evaluation aspect for the purpose of long-term and frequent monitoring of large-scale civil infrastructure during their service lives. Not only did it establish a real-world structural monitoring test-bed right at the heart of QUT Gardens Point Campus but it can also facilitate reliable and prompt protection for any built infrastructure system as well as the user community involved.

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A health-monitoring and life-estimation strategy for composite rotor blades is developed in this work. The cross-sectional stiffness reduction obtained by physics-based models is expressed as a function of the life of the structure using a recent phenomenological damage model. This stiffness reduction is further used to study the behavior of measurable system parameters such as blade deflections, loads, and strains of a composite rotor blade in static analysis and forward flight. The simulated measurements are obtained using an aeroelastic analysis of the composite rotor blade based on the finite element in space and time with physics-based damage modes that are then linked to the life consumption of the blade. The model-based measurements are contaminated with noise to simulate real data. Genetic fuzzy systems are developed for global online prediction of physical damage and life consumption using displacement- and force-based measurement deviations between damaged and undamaged conditions. Furthermore, local online prediction of physical damage and life consumption is done using strains measured along the blade length. It is observed that the life consumption in the matrix-cracking zone is about 12-15% and life consumption in debonding/delamination zone is about 45-55% of the total life of the blade. It is also observed that the success rate of the genetic fuzzy systems depends upon the number of measurements, type of measurements and training, and the testing noise level. The genetic fuzzy systems work quite well with noisy data and are recommended for online structural health monitoring of composite helicopter rotor blades.

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A new smart concrete aggregate design as a candidate for applications in structural health monitoring (SHM) of critical elements in civil infrastructure is proposed. The cement-based stress/strain sensor was developed by utilizing the stress/strain sensing properties of a magnetic microwire embedded in cement-based composite (MMCC). This is a contact-less type sensor that measures variations of magnetic properties resulting from stress variations. Sensors made of these materials can be designed to satisfy the specific demand for an economic way to monitor concrete infrastructure health. For this purpose, we embedded a thin magnetic microwire in the core of a cement-based cylinder, which was inserted into the concrete specimen under study as an extra aggregate. The experimental results show that the embedded MMCC sensor is capable of measuring internal compressive stress around the range of 1-30 MPa. Two stress sensing properties of the embedded sensor under uniaxial compression were studied: the peak amplitude and peak position of magnetic switching field. The sensitivity values for the amplitude and position within the measured range were 5 mV/MPa and 2.5 mu s/MPa, respectively.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

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The structural health monitoring (SHM) systems based on electromechanical (E/M) impedance technique have been widely investigated. Although many studies indicate the reliability of this technique, some practical considerations still have to be considered in real applications. This paper presents an experimental analysis of the effect of the structure area on the system's performance. The results indicate that the sensitivity of the system to detect damage decreases significantly when the host structure has large cross-section area. Copyright © 2009 by ASME.

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This paper presents a new approach for damage detection in structural health monitoring systems exploiting the coherence function between the signals from PZT (Lead Zirconate Titanate) transducers bonded to a host structure. The physical configuration of this new approach is similar to the configuration used in Lamb wave based methods, but the analysis and operation are different. A PZT excited by a signal with a wide frequency range acts as an actuator and others PZTs are used as sensors to receive the signal. The coherences between the signals from the PZT sensors are obtained and the standard deviation for each coherence function is computed. It is demonstrated through experimental results that the standard deviation of the coherence between the signals from the PZTs in healthy and damaged conditions is a very sensitive metric index to detect damage. Tests were carried out on an aluminum plate and the results show that the proposed methodology could be an excellent approach for structural health monitoring (SHM) applications.

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In this paper we present a system for aircraft structural health monitoring based on artificial immune systems with negative selection. Inspired by a biological process, the principle of discrimination proper/non-proper, identifies and characterizes the signs of structural failure. The main application of this method is to assist in the inspection of aircraft structures, to detect and characterize flaws and decision making in order to avoid disasters. We proposed a model of an aluminum beam to perform the tests of the method. The results obtained by this method are excellent, showing robustness and accuracy.

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Structural Health Monitoring (SHM) is the process of characterization for existing civil structures that proposes for damage detection and structural identification. It's based firstly on the collection of data that are inevitably affected by noise. In this work a procedure to denoise the measured acceleration signal is proposed, based on EMD-thresholding techniques. Moreover the velocity and displacement responses are estimated, starting from measured acceleration.

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El control del estado en el que se encuentran las estructuras ha experimentado un gran auge desde hace varias décadas, debido a que los costes de rehabilitación de estructuras tales como los oleoductos, los puentes, los edificios y otras más son muy elevados. En las últimas dos décadas, se han desarrollado una gran cantidad de métodos que permiten identificar el estado real de una estructura, basándose en modelos físicos y datos de ensayos. El ensayo modal es el más común; mediante el análisis modal experimental de una estructura se pueden determinar parámetros como la frecuencia, los modos de vibración y la amortiguación y también la función de respuesta en frecuencia de la estructura. Mediante estos parámetros se pueden implementar diferentes indicadores de daño. Sin embargo, para estructuras complejas y grandes, la implementación de metodologías basadas en la función de respuesta en frecuencia requeriría realizar hipótesis sobre la fuerza utilizada para excitar la estructura. Dado que el análisis modal operacional utiliza solamente las señales de respuesta del sistema para extraer los parámetros dinámicos estructurales y, por tanto, para evaluar el estado de una estructura, el uso de la transmisibilidad sería posible. En este sentido, dentro del análisis modal operacional, la transmisibilidad ha concentrado mucha atención en el mundo científico en la última década. Aunque se han publicado muchos trabajos sobre el tema, en esta Tesis se proponen diferentes técnicas para evaluar el estado de una estructura basándose exclusivamente en la transmisibilidad. En primer lugar, se propone un indicador de daño basado en un nuevo parámetro, la coherencia de transmisibilidad; El indicador se ha valido mediante resultados numéricos y experimentales obtenidos sobre un pórtico de tres pisos. En segundo lugar, la distancia de Mahalanobis se aplica sobre la transmisibilidad como procedimiento para detectar variaciones estructurales provocadas por el daño. Este método se ha validado con éxito sobre una viga libre-libre ensayada experimentalmente. En tercer lugar, se ha implementado una red neuronal basada en medidas de transmisibilidad como metodología de predicción de daño sobre una viga simulada numéricamente. ABSTRACT Structural health monitoring has experienced a huge development from several decades ago since the cost of rehabilitation of structures such as oil pipes, bridges and tall buildings is very high. In the last two decades, a lot of methods able to identify the real stage of a structure have been developed basing on both models and experimental data. Modal testing is the most common; by carrying out the experimental modal analysis of a structure, some parameters, such as frequency, mode shapes and damping, as well as the frequency response function of the structure can be obtained. From these parameters, different damage indicators have been proposed. However, for complex and large structures, any frequency domain approach that relies on frequency response function estimation would be of difficult application since an assumption of the input excitations to the system should be carried out. Operational modal analysis uses only output signals to extract the structural dynamic parameters and, therefore, to identify the structural stage. In this sense, within operational modal analysis, transmissibility has attracted a lot of attention in the scientific field in the last decade. In this work new damage detection approaches based on transmissibility are developed. Firstly, a new theory of transmissibility coherence is developed and it is tested with a three-floor-structure both in simulation and in experimental data analysis; secondly, Mahalanobis distance is taken into use with the transmissibility, and a free-free beam is used to test the approach performance; thirdly, neural networks are used in transmissibility for structural health monitoring; a simulated beam is used to validate the proposed method.

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El control del estado en el que se encuentran las estructuras ha experimentado un gran auge desde hace varias décadas, debido a que los costes de rehabilitación de estructuras tales como los oleoductos, los puentes, los edificios y otras más son muy elevados. En las últimas dos décadas, se han desarrollado una gran cantidad de métodos que permiten identificar el estado real de una estructura, basándose en modelos físicos y datos de ensayos. El ensayo modal es el más común; mediante el análisis modal experimental de una estructura se pueden determinar parámetros como la frecuencia, los modos de vibración y la amortiguación y también la función de respuesta en frecuencia de la estructura. Mediante estos parámetros se pueden implementar diferentes indicadores de daño. Sin embargo, para estructuras complejas y grandes, la implementación de metodologías basadas en la función de respuesta en frecuencia requeriría realizar hipótesis sobre la fuerza utilizada para excitar la estructura. Dado que el análisis modal operacional utiliza solamente las señales de respuesta del sistema para extraer los parámetros dinámicos estructurales y, por tanto, para evaluar el estado de una estructura, el uso de la transmisibilidad sería posible. En este sentido, dentro del análisis modal operacional, la transmisibilidad ha concentrado mucha atención en el mundo científico en la última década. Aunque se han publicado muchos trabajos sobre el tema, en esta Tesis se proponen diferentes técnicas para evaluar el estado de una estructura basándose exclusivamente en la transmisibilidad. En primer lugar, se propone un indicador de daño basado en un nuevo parámetro, la coherencia de transmisibilidad; El indicador se ha valido mediante resultados numéricos y experimentales obtenidos sobre un pórtico de tres pisos. En segundo lugar, la distancia de Mahalanobis se aplica sobre la transmisibilidad como procedimiento para detectar variaciones estructurales provocadas por el daño. Este método se ha validado con éxito sobre una viga libre-libre ensayada experimentalmente. En tercer lugar, se ha implementado una red neuronal basada en medidas de transmisibilidad como metodología de predicción de daño sobre una viga simulada numéricamente. ABSTRACT Structural health monitoring has experienced a huge development from several decades ago since the cost of rehabilitation of structures such as oil pipes, bridges and tall buildings is very high. In the last two decades, a lot of methods able to identify the real stage of a structure have been developed basing on both models and experimental data. Modal testing is the most common; by carrying out the experimental modal analysis of a structure, some parameters, such as frequency, mode shapes and damping, as well as the frequency response function of the structure can be obtained. From these parameters, different damage indicators have been proposed. However, for complex and large structures, any frequency domain approach that relies on frequency response function estimation would be of difficult application since an assumption of the input excitations to the system should be carried out. Operational modal analysis uses only output signals to extract the structural dynamic parameters and, therefore, to identify the structural stage. In this sense, within operational modal analysis, transmissibility has attracted a lot of attention in the scientific field in the last decade. In this work new damage detection approaches based on transmissibility are developed. Firstly, a new theory of transmissibility coherence is developed and it is tested with a three-floor-structure both in simulation and in experimental data analysis; secondly, Mahalanobis distance is taken into use with the transmissibility, and a free-free beam is used to test the approach performance; thirdly, neural networks are used in transmissibility for structural health monitoring; a simulated beam is used to validate the proposed method.