Optimal Frequency Range Selection for PZT Transducers in Impedance-Based SHM Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Experimental damage detection grammian matrix

Structural Health Monitoring (SHM) has diverse potential applications, and many groups work in the development of tools and techniques for monitoring structural performance. These systems use arrays of sensors and can be integrated with remote or local computers. There are several different approaches that can be used to obtain information about the existence, location and extension of faults by non destructive tests. In this paper an experimental technique is proposed for damage location based on an observability grammian matrix. The dynamic properties of the structure are identified through experimental data using the eigensystem realization algorithm (ERA). Experimental tests were carried out in a structure through varying the mass of some elements. Output signals were obtained using accelerometers.

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 damage detection by fuzzy clustering

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.

Sliding mode for detection and accommodation of computation time delay fault

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mycobacterium DNA detection in liver and skin of a horse with generalized sarcoidosis

Sarcoidosis is a rare equine skin disease characterized primarily by an exfoliative and granulomatous dermatitis but also presenting granulomatous inflammation of multiple systems. The current report presents the clinical and histopathological findings of sarcoidosis in a 16-year-old American Quarter Horse gelding with nested polymerase chain reaction Mycobacterium spp. DNA detection within hepatic and skin samples. Mycobacterium spp. may play a role in the pathogenesis of equine sarcoidosis as has been proposed for human sarcoidosis.

Reproducibility and accuracy of the ICDAS-II for occlusal caries detection

Objectives:The aim of this in vitro study was to assess the inter- and intra-examiner reproducibility and the accuracy of the International Caries Detection and Assessment System-II (ICDAS-II) in detecting occlusal caries.Methods:One hundred and sixty-three molars were independently assessed twice by two experienced dentists using the 0- to 6-graded ICDAS-II. The teeth were histologically prepared and classified using two different histological systems [Ekstrand et al. (1997) Caries Research vol. 31, pp. 224-231; Lussi et al. (1999) Caries Research vol. 33, pp. 261-266] and assessed for caries extension. Sensitivity, specificity, accuracy and area under the ROC curve (A(z)) were obtained at D(2) and D(3) thresholds. Unweighted kappa coefficient was used to assess inter- and intra-examiner reproducibility.Results:For the Ekstrand et al. histological classification the sensitivity was 0.99 and 1.00, specificity 1.00 and 0.69 and accuracy 0.99 and 0.76 at D(2) and D(3), respectively. For the Lussi et al. histological classification the sensitivity was 0.91 and 0.75, specificity 0.47 and 0.62 and accuracy 0.86 and 0.68 at D(2) and D(3), respectively. The A(z) varied from 0.54 to 0.73. The inter- and intra-examiner kappa values were 0.51 and 0.58, respectively.Conclusions:ICDAS-II presented good reproducibility and accuracy in detecting occlusal caries, especially caries lesions in the outer half of the enamel.

Detection of hybrids and genetic introgression in wild stocks of two catfish species (Siluriformes: Pimelodidae): The impact of hatcheries in Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Natural radionuclides in major aquifer systems of the Parana sedimentary basin, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Detection of Toxoplasma gondii in the reproductive system of male goats

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Leak detection in petroleum pipelines using a fuzzy system

A methodology for pipeline leakage detection using a combination of clustering and classification tools for fault detection is presented here. A fuzzy system is used to classify the running mode and identify the operational and process transients. The relationship between these transients and the mass balance deviation are discussed. This strategy allows for better identification of the leakage because the thresholds are adjusted by the fuzzy system as a function of the running mode and the classified transient level. The fuzzy system is initially off-line trained with a modified data set including simulated leakages. The methodology is applied to a small-scale LPG pipeline monitoring case where portability, robustness and reliability are amongst the most important criteria for the detection system. The results are very encouraging with relatively low levels of false alarms, obtaining increased leakage detection with low computational costs. (c) 2005 Elsevier B.V. All rights reserved.

Comparison of different clean-up procedures for the determination of N-methylcarbamate insecticides in vegetable matrices by high-performance liquid chromatography with UV detection

Several clean-up procedures which included the use of glass chromatography columns (silica gel, alumina, Florisil, silanized Celite-charcoal), Sep-Pak cartridges and standard solutions were compared for the determination of the following N-methylcarbamate (NMC) insecticides: aldicarb, carbaryl, carbofuran, methomyl and propoxur. According to recovery results of the compounds after elution in a glass column, the most efficient systems employed 4.6% deactivated alumina and a silanized Celite-charcoal (4:1) as adsorbents, using dichloromethane-methanol (99:1) and toluene-acetonitrile (75:25) mixtures, respectively, as binary eluents. The recoveries of the compounds studied varied from 84 to 120%. Comparable recoveries (75-100%) for Sep-Pak cartridges in normal phase (NH2, CN) and reversed phase (C-8) were observed. Different temperatures were tested during the concentration step in a rotary evaporator, and we verified a strong influence of this parameter on the stability of some compounds, such as carbofuran and carbaryl. Recovery studies employing the best clean up procedures were performed at the Brazilian agricultural level in potato and carrot samples; Validation methodology of the US Food and Drug Administration was adapted for the N-methylcarbamate analysis. Their recoveries ranged between 79 and 93% with coefficients of variation of 2.3-8%. (C) 1998 Elsevier B.V. B.V.

Detection of catechol using mixed Langmuir-Blodgett films of a phospholipid and phthalocyanines as voltammetric sensors

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Automation in fault detection using neural network and model updating

In this article, an implementation of structural health monitoring process automation based on vibration measurements is proposed. The work presents an alternative approach which intent is to exploit the capability of model updating techniques associated to neural networks to be used in a process of automation of fault detection. The updating procedure supplies a reliable model which permits to simulate any damage condition in order to establish direct correlation between faults and deviation in the response of the model. The ability of the neural networks to recognize, at known signature, changes in the actual data of a model in real time are explored to investigate changes of the actual operation conditions of the system. The learning of the network is performed using a compressed spectrum signal created for each specific type of fault. Different fault conditions for a frame structure are evaluated using simulated data as well as measured experimental data.

Adaptative techniques for the human faces detection

This paper presents results from an efficient approach to an automatic detection and extraction of human faces from images with any color, texture or objects in background, that consist in find isosceles triangles formed by the eyes and mouth.