Monitoring of diclofenac with biomimetic sensor in batch and FIA systems

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

DEVELOPMENT OF A BIOMIMETIC SENSOR MODIFIED WITH HEMIN AND GRAPHENE OXIDE FOR MONITORING OF CARBOFURAN IN FOOD

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

Construction of a chamber for in situ monitoring of the drying process of gels and porous solids

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

On-line monitoring of electrochemical degradation of paracetamol through a biomimetic sensor

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

Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg-Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as "sharp''(with cutting capacity) or "dull''(with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.

One-hundred days of monitoring of patients undergoing hematopoietic stem cell transplantation: events of metabolic syndrome

Objective This study assessed early manifestations of metabolic syndrome determinants in patients submitted to hematopoietic stem cell transplantation. Methods Twenty-seven individuals participated in the study (20 with autologous and 7 with allogeneic hematopoietic stem cell transplantation). Anthropometric variables and biochemical indicators of lipid and glucose metabolism were determined before and 100 days after hematopoietic stem cell transplantation.Results The mean total cholesterol (p=0.086), very low density lipoprotein-cholesterol (p=0.069) and triglycerides (p=0.086) of all patients did not change significantly between the two study periods, but when the patients were separated by type of hematopoietic stem cell transplantation, triglycerides and very low density lipoprotein-cholesterol were close to the critical level of significance for individuals with allogeneic hematopoietic stem cell transplantation (p=0.060) and total cholesterol was significant in individuals with autologous hematopoietic stem cell transplantation (p=0.027). Anthropometric variables did not change significantly between before and 100 days after hematopoietic stem cell transplantation. Conclusion Metabolic syndrome risk factors may be associated with lipid metabolism in the early phase of allogeneic and autologous hematopoietic stem cell transplantation.

Influence of Excitation Signal on Impedance-based Structural Health Monitoring

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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.

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)

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.

Adaptive filter feature identification for structural health monitoring in an aeronautical panel

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Identification of hybrids between Neotropical fish Leporinus macrocephalus and Leporinus elongatus by PCR-RFLP and multiplex-PCR: Tools for genetic monitoring in aquaculture

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

Utilization of electrochemical impedance spectroscopy for monitoring bornite (CU5FeS4) oxidation by Acidithiobacillus ferrooxidans

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

Real-time monitoring and kinetic parameter estimation of the affinity interaction of jArtinM and rArtinM with peroxidase glycoprotein by the electrogravimetric technique

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

Impedance-based structural health monitoring with artificial neural networks

This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically >30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, multiple sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with experimental examples, investigations on a massive quarter scale model of a steel bridge section and a space truss structure, in order to verify the performance of this proposed methodology.