Automatic system for thermal damage detection in manufacturing process with Internet monitoring

This work involved the development of a smart system dedicated to surface burning detection in the grinding process through constant monitoring of the process by acoustic emission and electrical power signals. A program in Visual Basic® for Windows® was developed, which collects the signals through an analog-digital converter and further processes them using burning detection algorithms already known. Three other parameters are proposed here and a comparative study carried out. When burning occurs, the newly developed software program sends a control signal warning the operator or interrupting the process, and delivers process information via the Internet. Parallel to this, the user can also interfere in the process via Internet, changing parameters and/or monitoring the grinding process. The findings of a comparative study of the various parameters are also discussed here. Copyright © 2006 by ABCM.

Experimental damage detection using system norms

Nowadays there is great interest in structural damage detection in systems using nondestructive tests. Once the failure is detected, as for instance a crack, it is possible to take providences. There are several different approaches that can be used to obtain information about the existence, location and extension of the fault in the system by non-destructive tests. Among these methodologies, one can mention different optimization techniques, as for instance classical methods, genetic algorithms, neural networks, etc. Most of these techniques, which are based on element-byelement adjustments of a finite element (FE) model, take advantage of the dynamic behavior of the model. However, in practical situations, usually, is almost impossible to obtain an accuracy model. In this paper, it is proposed an experimental technique for damage location. This technique is based on H: norm to obtain the damage location. The dynamic properties of the structure were identified using experimental data by eigensystem realization algorithm (ERA). The experimental test was carried out in a beam structure through varying the mass of an element. For the output signal was used a piezoelectric sensor. The signal of input of sine form was generated through SignalCalc® software.

A multicommuted flow-based system for hydrogen peroxide determination by chemiluminescence detection using a photodiode

A simple, rapid, and automated assay for hydrogen peroxide in pharmaceutical samples was developed by combining the multicommutation system with a chemiluminescence (CL) detector. The detection was performed using a spiral flow-cell reactor made from polyethylene tubing that was positioned in front of a photodiode. It allows the rapid mixing of CL reagent and analyte and simultaneous detection of the emitted light. The chemiluminescence was based on the reaction of luminol with hydrogen peroxide catalyzed by hexacyanoferrate(III). The feasibility of the flow system was ascertained by analyzing a set of pharmaceutical samples. A linear response within the range of 2.2-210 μmol l-1 H2O2 with a LD of 1.8 μmol l-1 H2O2 and coefficient of variations smaller than 0.8% for 1.0×10-5 mol l-1 and 6.8×10-5 mol l-1 hydrogen peroxide solutions (n=10) were obtained. Reagents consumption of 90 μg of luminol and 0.7 mg of hexacyanoferrate(III) per determination and sampling rate of 200 samples per hour were also achieved. Copyright © Taylor & Francis Group, LLC.

Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids. © 2013 IEEE.