Adaptive filter feature identification for structural health monitoring in aeronautical panel

This paper presents an approach for structural health monitoring (SHM) by using adaptive filters. The experimental signals from different structural conditions provided by piezoelectric actuators/sensors bonded in the test structure are modeled by a discrete-time recursive least square (RLS) filter. The biggest advantage to use a RLS filter is the clear possibility to perform an online SHM procedure since that the identification is also valid for non-stationary linear systems. An online damage-sensitive index feature is computed based on autoregressive (AR) portion of coefficients normalized by the square root of the sum of the square of them. The proposed method is then utilized in a laboratory test involving an aeronautical panel coupled with piezoelectric sensors/actuators (PZTs) in different positions. A hypothesis test employing the t-test is used to obtain the damage decision. The proposed algorithm was able to identify and localize the damages simulated in the structure. The results have shown the applicability and drawbacks the method and the paper concludes with suggestions to improve it. ©2010 Society for Experimental Mechanics Inc.

Damage detection in nonlinear structures using discrete-time volterra series

Structural damage identification is basically a nonlinear phenomenon; however, nonlinear procedures are not used currently in practical applications due to the complexity and difficulty for implementation of such techniques. Therefore, the development of techniques that consider the nonlinear behavior of structures for damage detection is a research of major importance since nonlinear dynamical effects can be erroneously treated as damage in the structure by classical metrics. This paper proposes the discrete-time Volterra series for modeling the nonlinear convolution between the input and output signals in a benchmark nonlinear system. The prediction error of the model in an unknown structural condition is compared with the values of the reference structure in healthy condition for evaluating the method of damage detection. Since the Volterra series separate the response of the system in linear and nonlinear contributions, these indexes are used to show the importance of considering the nonlinear behavior of the structure. The paper concludes pointing out the main advantages and drawbacks of this damage detection methodology. © (2013) Trans Tech Publications.

Monitoramento da Integridade Estrutural de Sistemas Mecânicos via Observador de Estado Modal

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aplicação da análise de séries temporais para detecção e prognóstico de danos em estruturas inteligentes

Pós-graduação em Engenharia Mecânica - FEIS

Cooperativismo e precarização do trabalho: um estudo aplicado na Cooperativa Recicla Conquista em Vitória da Conquista/BA

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Non-destructive evaluation tool for monitoring and detection of structural damage by using neural network.

This work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.

Structural variations in root and stem wood of Styrax (Styracaceae) from Brazilian forest and cerrado

The genus Styrax L. (Styracaceae) has a wide distribution in Brazil, occurring in diverse ecosystems. To get a better insight into the ecological adaptations of wood structure, we studied two species, S. camporum and S.ferrugineus from the cerrado, and three species, S. latifolium, S. martii and S. leprosus from the Atlantic forest. For each species, the wood of root and stem was analyzed separately and observations included qualitative as well as quantitative wood characteristics. The results show that there were significant anatomical differences between the forest and cerrado species as well as between the root and stem wood within single species. Quantitatively, the most informative features in the root wood that separated the forest from the cerrado, species were diameter, length and number of vessels, length of fibres, and width and frequency of rays. In the stem wood, length and frequency of vessels, length of fibres, and width and frequency of rays were the most informative features. In contrast to the forest species, which had larger vessel diameters in their stem wood, the cerrado species had larger vessel diameters in their root wood. The calculated vulnerability index indicates that all Sryrax species have adaptations to mesic conditions. The cerrado species had the smallest index values, which could be related to the seasonally dry condition of this environment.

SnO2 nanocrystals synthesized by microwave-assisted hydrothermal method: towards a relationship between structural and optical properties

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Chondroitin sulfate proteoglycans are structural renewable constituents of the rabbit vitreous body

Purpose: To characterize the vitreous intrinsic proteoglycans, investigate their dynamics, and examine their role in the supramolecular organization of the vitreous. Methods: Vitreous from normal rabbits was collected and processed for observation with the transmission electron microscope after treatment with glycosidases. Also, rabbits were injected intravitreally with [S-35]-sodium sulfate and sacrificed at several time intervals after the injection. Proteoglycans (PGs) were assayed in the vitreous supernatant or in whole samples extracted with guanidine hydrochloride by polyacrylamide or agarose gel electrophoresis, followed respectively by fluorography or autoradiography, and ion-exchange chromatography and gel-filtration chromatography, combined with glycolytic treatment of the samples. The sulfated glycosaminoglycans (GAGs) were characterized by agarose gel electrophoresis after treating vitreous samples with protease and specific glycosidases. Results: the electron microscopic study revealed a network with hyaluronic acid ( HA) as thin threads coating and connecting collagen fibrils. The elimination of the HA coat showed chondroitin sulfate granules (8-25 nm) arranged at regular intervals on the fibril surface. The chondroitinase ABC digestion, besides removing the granules, also caused the formation of thicker bundles of the collagen fibrils. The PG and GAG analysis indicated that there are three renewable PGs in the vitreous ( e. g., one heparan-and two chondroitin-sulfate ones). Conclusions: At least one of the chondroitin sulfate PGs is involved in the interactions that occur in the vitreous structure, mainly by providing adequate spacing between the collagen fibrils, a condition that is probably required for the transparency of the vitreous.

Application of a model updating technique for health condition monitoring and damage detection of flexible structures

This paper discusses the application of a damage detection methodology to monitor the location and extent of partial structural damage. The methodology combines, in an iterative way, the model updating technique based on frequency response functions (FRF) with monitoring data aiming at identifying the damage area of the structure. After the updating procedure reaches a good correlation between the models, it compares the parameters of the damage structure with those of the undamaged one to find the deteriorated area. The influence of the FEM mesh size on the evaluation of the extent of the damage has also been discussed. The methodology is applied using real experimental data from a spatial frame structure.

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

Influence of microwave energy on structural and piezoelectric response of Bi 4Ti 3O 12 ceramics

Bismuth titanate ceramics (Bi 4Ti 3O 12) with 10 wt% in excess of bismuth (BIT10) were prepared by the polymeric precursor method and sinterized in microwave (MW) and conventional furnaces (CF). The effect of microwave energy on structural and electrical behavior of BIT10 ceramics was investigated by means of X-ray diffraction (XRD), Scanning electron microscopy (SEM) and electrical measurements. The results of the BIT10 ceramics processed in the microwave furnace (MW) showed a high structural organization compared to conventional treatment (CF). Size of grains and dieletrical properties are influenced by annealing conditions while coercitive field is not dependent on it. The maximum dielectric permittivity (12000) was obtained for the sample sintered in the microwave furnace. Piezoelectric force microscopy images reveals that in-plane response may not change its sign upon polarization switching, while the out-of-plane response does with the influence of microwave energy. Copyright © 2010 American Scientific Publishers All rights reserved.

Versatile and easy-to-assemble measurement system for impedance-based structural health monitoring

In this paper we present a versatile and easy-to-assemble measurement system for structural health monitoring (SHM) based on the electromechanical impedance (EMI) technique. The hardware of the proposed system consists only of a common data acquisition (DAQ) device with external resistors and allows real-time data acquisition from multiple sensors. Besides the low-cost compared to conventional impedance analyzers, the hardware and the software are simple and easier to implement than other measurement systems that have been recently proposed.

Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building

This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This methodology can assist professionals in the inspection of mechanical and civil structures, to identify and characterize flaws, in order to perform preventative maintenance to ensure the integrity of the structure and decision-making. In order to evaluate the methodology was made modeling a two-story building and several situations were simulated (base-line condition and improper conditions), yielding a database of signs, which were used as input data for the negative selection algorithm. The results obtained by the present method efficiency, robustness and accuracy.

Analysis of Structural Integrity Using an ARTMAP-Fuzzy Artificial Neural Network

This paper presents the application of artificial neural networks in the analysis of the structural integrity of a building. The main objective is to apply an artificial neural network based on adaptive resonance theory, called ARTMAP-Fuzzy neural network and apply it to the identification and characterization of structural failure. This methodology can help professionals in the inspection of structures, to identify and characterize flaws in order to conduct preventative maintenance to ensure the integrity of the structure and decision-making. In order to validate the methodology was modeled a building of two walk, and from this model were simulated various situations (base-line condition and improper conditions), resulting in a database of signs, which were used as input data for ARTMAP-Fuzzy network. The results show efficiency, robustness and accuracy.