Design and implementation of wireless sensor networks for impedance-based structural health monitoring using ZigBee and Global System for Mobile Communications

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Application of Volterra series in nonlinear mechanical system identification and in structural health monitoring problems

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

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

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

MICROBIOLOGICAL CONTAMINATION of A HEMODIALYSIS CENTER WATER DISTRIBUTION SYSTEM

The microbiological monitoring of the water used for hemodialysis is extremely important, especially because of the debilitated immune system of patients suffering from chronic renal insufficiency. To investigate the occurrence and species diversity of bacteria in waters, water samples were collected monthly from a hemodialysis center in upstate São Paulo and tap water samples at the terminal sites of the distribution system was sampled repeatedly (22 times) at each of five points in the distribution system; a further 36 samples were taken from cannulae in 19 hemodialysis machines that were ready for the next patient, four samples from the reuse system and 13 from the water storage system. To identify bacteria, samples were filtered through 0.22 mu m-pore membranes; for mycobacteria, 0.45 mu m pores were used. Conventional microbiological and molecular methods were used in the analysis. Bacteria were isolated from the distribution system (128 isolates), kidney machine water (43) and reuse system (3). Among these isolates, 32 were Gram-positive rods, 120 Gram-negative rods, 20 Gram-positive cocci and 11 mycobacteria. We propose the continual monitoring of the water supplies in hemodialysis centers and the adoption of effective prophylactic measures that minimize the exposure of these immunodeficient patients to contaminated sources of water.

An Intelligent System for Petroleum Well Drilling Cutting Analysis

Cutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cutting's concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cutting's images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cutting's volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multi layer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last one's efficiency.

In-process grinding monitoring by acoustic emission

This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate data acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. Many statistics have shown effective to detect burn, such as the root mean square (RMS), correlation of the AE, constant false alarm (CFAR), ratio of power (ROP) and mean-value deviance (MVD). However, the CFAR, ROP, Kurtosis and correlation of the AE have been presented more sensitive than the RMS.

Influence of Excitation Signal on Impedance-based Structural Health Monitoring

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Automatic dimming multi-lamp fluorescent management system with active input power factor corrector stage

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

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 Health Monitoring in Smart Structures Through Time Series Analysis

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

Microbiological Evaluation of Chicken Carcasses in an Immersion Chilling System with Water Renewal at 8 and 16 Hours

Since 2004, Brazil has been the leading exporter of chicken. Because of the importance of this sector in the Brazilian economy, food safety must be ensured by control and monitoring of the production stages susceptible to contamination, such as the chilling process. The goal of this study was to evaluate changes in microbial levels on chicken carcasses and in chilling water after immersion in a chilling system for 8 and 16 h during commercial processing. An objective of the study was to encourage discussion regarding the Brazilian Ministry of Agriculture Livestock and Food Supply regulation that requires chicken processors to completely empty, clean, and disinfect each tank of the chilling system after every 8-h shift. Before and after immersion carcasses were collected and analyzed for mesophilic bacteria, Enterobacteriaceae, conforms, and Escherichia coli. Samples of water from the chilling system were also analyzed for residual free chlorine. The results do not support required emptying of the chiller tank after 8 h; these tanks could be emptied after 16 h. The results for all carcasses tested at the 8- and 16-h time points indicated no significant differences in the microbiological indicators evaluated. These data provide both technical and scientific support for discussing changes in federal law regarding the management of immersion chilling water systems used as part of the poultry processing line.

Allium cepa test in environmental monitoring: A review on its application

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

Effects of Spider Venom Toxin PWTX-I (6-Hydroxytrypargine) on the Central Nervous System of Rats

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

Petroleum well drilling monitoring through cutting image analysis and artificial intelligence techniques

Petroleum well drilling monitoring has become an important tool for detecting and preventing problems during the well drilling process. In this paper, we propose to assist the drilling process by analyzing the cutting images at the vibrating shake shaker, in which different concentrations of cuttings can indicate possible problems, such as the collapse of the well borehole walls. In such a way, we present here an innovative computer vision system composed by a real time cutting volume estimator addressed by support vector regression. As far we know, we are the first to propose the petroleum well drilling monitoring by cutting image analysis. We also applied a collection of supervised classifiers for cutting volume classification. (C) 2010 Elsevier Ltd. All rights reserved.

Quartz Crystal Microbalance monitoring the real-time binding of lectin with carbohydrate with high and low molecular mass

Quartz Crystal Microbalance (QCM) was used to monitor the mass changes on a quartz crystal surface containing immobilized lectins that interacted with carbohydrates. The strategy for lectin immobilization was developed on the basis of a multilayer system composed of Au-cystamine-glutaraldehyde-lectin. Each step of the immobilization procedure was confirmed by FTIR analysis. The system was used to study the interactions of Concanavalin A (ConA) with maltose and Jacalin with Fetuin. The real-time binding of different concentrations of carbohydrate to the immobilized lectin was monitored by means of QCM measurements and the data obtained allowed for the construction of Langmuir isotherm curves. The association constants determined for the specific interactions analyzed here were (6.4 +/- 0.2) X 10(4) M-1 for Jacalin-Fetuin and (4.5 +/- 0.1) x 10(2) M-1 for ConA-maltose. These results indicate that the QCM constitutes a suitable method for the analysis of lectin-carbohydrate interactions, even when assaying low molecular mass ligands such as disaccharides. Published by Elsevier B.V.