Production grinding burn detection using acoustic emission and electric power signals

This work uses a monitoring system based on a PC platform, where the acoustic emission and electric power signals generated during the grinding process are used to investigate superficial burning occurrence in a surface grinding operation using two types of steel, three grinding conditions and an Al203 vitrified grinding wheel. Acoustic emission signals on the workpiece and grinding power were measured during a surface plunge operation until the grinding burn happened. From the results the standard deviation of the acoustic emission signal and the maximum electric power were calculated for each grinding pass. The proposed DPO parameter is the product between the power level and acoustic emission standard deviation. The results show that both signals can be used for burning detection, and the parameter DPO is the best indicator for the burning studied in this work. This can be explained by the high dispersion of the acoustic emission RMS level associated to the high power consumption when the grinding wheel lose its sharpness.

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year

From irradiance to output power fluctuations: the pv plant as a low pass filter

The power generated by large grid-connected photovoltaic (PV) plants depends greatly on the solar irradiance. This paper studies the effects of the solar irradiance variability analyzing experimental 1-s data collected throughout a year at six PV plants, totaling 18 MWp. Each PV plant was modeled as a first order filter function based on an analysis in the frequency domain of the irradiance data and the output power signals. An empiric expression which relates the filter parameters and the PV plant size has been proposed. This simple model has been successfully validated precisely determining the daily maximum output power fluctuation from incident irradiance measurements.

Application of high voltage, high frequency pulsed electromagnetic field on cortical bone tissue

Over the last few decades, electric and electromagnetic fields have achieved important role as stimulator and therapeutic facility in biology and medicine. In particular, low magnitude, low frequency, pulsed electromagnetic field has shown significant positive effect on bone fracture healing and some bone diseases treatment. Nevertheless, to date, little attention has been paid to investigate the possible effect of high frequency, high magnitude pulsed electromagnetic field (pulse power) on functional behaviour and biomechanical properties of bone tissue. Bone is a dynamic, complex organ, which is made of bone materials (consisting of organic components, inorganic mineral and water) known as extracellular matrix, and bone cells (live part). The cells give the bone the capability of self-repairing by adapting itself to its mechanical environment. The specific bone material composite comprising of collagen matrix reinforced with mineral apatite provides the bone with particular biomechanical properties in an anisotropic, inhomogeneous structure. This project hypothesized to investigate the possible effect of pulse power signals on cortical bone characteristics through evaluating the fundamental mechanical properties of bone material. A positive buck-boost converter was applied to generate adjustable high voltage, high frequency pulses up to 500 V and 10 kHz. Bone shows distinctive characteristics in different loading mode. Thus, functional behaviour of bone in response to pulse power excitation were elucidated by using three different conventional mechanical tests applying three-point bending load in elastic region, tensile and compressive loading until failure. Flexural stiffness, tensile and compressive strength, hysteresis and total fracture energy were determined as measure of main bone characteristics. To assess bone structure variation due to pulse power excitation in deeper aspect, a supplementary fractographic study was also conducted using scanning electron micrograph from tensile fracture surfaces. Furthermore, a non-destructive ultrasonic technique was applied for determination and comparison of bone elasticity before and after pulse power stimulation. This method provided the ability to evaluate the stiffness of millimetre-sized bone samples in three orthogonal directions. According to the results of non-destructive bending test, the flexural elasticity of cortical bone samples appeared to remain unchanged due to pulse power excitation. Similar results were observed in the bone stiffness for all three orthogonal directions obtained from ultrasonic technique and in the bone stiffness from the compression test. From tensile tests, no significant changes were found in tensile strength and total strain energy absorption of the bone samples exposed to pulse power compared with those of the control samples. Also, the apparent microstructure of the fracture surfaces of PP-exposed samples (including porosity and microcracks diffusion) showed no significant variation due to pulse power stimulation. Nevertheless, the compressive strength and toughness of millimetre-sized samples appeared to increase when the samples were exposed to 66 hours high power pulsed electromagnetic field through screws with small contact cross-section (increasing the pulsed electric field intensity) compare to the control samples. This can show the different load-bearing characteristics of cortical bone tissue in response to pulse power excitation and effectiveness of this type of stimulation on smaller-sized samples. These overall results may address that although, the pulse power stimulation can influence the arrangement or the quality of the collagen network causing the bone strength and toughness augmentation, it apparently did not affect the mineral phase of the cortical bone material. The results also confirmed that the indirect application of high power pulsed electromagnetic field at 500 V and 10 kHz through capacitive coupling method, was athermal and did not damage the bone tissue construction.

Estudo da degradação de fibras óticas sob o efeito de potências elevadas

Os sistemas de comunicações óticas têm evoluído rapidamente e, como consequência, a potência dos sinais óticos propagados na fibra tem vindo a aumentar. A propagação de sinais de elevada potência pode resultar na degradação e/ou destruição da fibra ótica. O presente trabalho tem como objetivo o estudo do impacto da propagação de sinais óticos com elevadas potências na degradação e redução do tempo de vida útil das fibras óticas. Estudou-se este tipo de degradação das fibras óticas a dois níveis: degradação do revestimento de fibras sujeitas a curvaturas apertadas e ignição e propagação do efeito rastilho que leva a destruição da fibra ótica. Para a concretização deste estudo, caracterizou-se experimentalmente o aumento de temperatura no revestimento de fibras óticas sujeitas a curvaturas de diâmetro reduzido e na presença de sinais com elevada potência. Tendo-se desenvolvido um modelo analítico para descrever o aquecimento do revestimento da fibra. No âmbito do efeito rastilho em fibra ótica, estudou-se experimentalmente, em diversos tipos de fibra, as principais propriedades deste fenómeno, nomeadamente, a potência ótica de limiar necessária para a sua ignição e propagação, a velocidade de propagação e as características da cadeia de bolhas formada no núcleo da fibra ótica durante a propagação deste efeito. Desenvolveu-se, ainda, um modelo teórico para simular a propagação do efeito rastilho, tendo sido validado e complementado através de resultados experimentais. Por fim, desenvolveram-se e implementaram-se técnicas para detetar e interromper a propagação do efeito rastilho, como forma de limitar a extensão de fibra destruída e proteger os equipamentos óticos da rede.

Estudo da degradação do tempo de vida das fibras óticas e o seu impacte no desempenho das redes de comunicações

Este trabalho focou-se no estudo do impacte das condições ambientais, de instalação e de utilização na degradação da fibra ótica, que frequentemente resultam na redução do desempenho das fibras óticas. Entre este fatores, foram estudados os efeitos de ambientes agressivos para o revestimento da fibra, nomeadamente no tempo de vida e resistência. Foi também estudado o efeito da propagação de sinais óticos de elevadas potências em curvaturas apertadas e a sua influência na degradação do desempenho da fibra ótica. Ainda neste âmbito, foi também estudado o desempenho de fibras óticas insensíveis a curvtura e fibras dopadas com Érbio, sendo analisada a dinâmica do efeito rastilho nestas fibras. Como parte integrante das redes óticas, os conetores óticos são de extrema importância na sua estrutura. O seu desempenho será refletido na qualidade do serviço da rede, e por isso é determinante estudar os fatores que contribuem para a sua degradação e mau funcionamento. Assim, este trabalho apresenta um estudo do comportamento de conetores óticos perante situações de mau manuseamento (como são limpeza insuficiente e degradação física da face final). Em adição foi também dado ênfase à reutilização de fibra danificada pelo efeito rastilho no desenvolvimento de sensores, passíveis de serem utilizados na monitorização de índice de refração, pressão hidrostática, tensão ou alta temperatura. Este procedimento surge como uma solução de baixo custo para o desenvolvimento de sensores em fibra ótica a partir de fibra danificada e inutilizável para as suas habituais aplicações em transmissão e/ou reflexão de sinais óticos.

A Faster 2D Technique for the design of Combinational Digital Circuits Using Genetic Algorithm

Combinational digital circuits can be evolved automatically using Genetic Algorithms (GA). Until recently this technique used linear chromosomes and and one dimensional crossover and mutation operators. In this paper, a new method for representing combinational digital circuits as 2 Dimensional (2D) chromosomes and suitable 2D crossover and mutation techniques has been proposed. By using this method, the convergence speed of GA can be increased significantly compared to the conventional methods. Moreover, the 2D representation and crossover operation provides the designer with better visualization of the evolved circuits. In addition to this, a technique to display automatically the evolved circuits has been developed with the help of MATLAB

Towards the development of a new wavelet for ECG classification

In this paper an attempt has been made to determine the number of Premature Ventricular Contraction (PVC) cycles accurately from a given Electrocardiogram (ECG) using a wavelet constructed from multiple Gaussian functions. It is difficult to assess the ECGs of patients who are continuously monitored over a long period of time. Hence the proposed method of classification will be helpful to doctors to determine the severity of PVC in a patient. Principal Component Analysis (PCA) and a simple classifier have been used in addition to the specially developed wavelet transform. The proposed wavelet has been designed using multiple Gaussian functions which when summed up looks similar to that of a normal ECG. The number of Gaussians used depends on the number of peaks present in a normal ECG. The developed wavelet satisfied all the properties of a traditional continuous wavelet. The new wavelet was optimized using genetic algorithm (GA). ECG records from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database have been used for validation. Out of the 8694 ECG cycles used for evaluation, the classification algorithm responded with an accuracy of 97.77%. In order to compare the performance of the new wavelet, classification was also performed using the standard wavelets like morlet, meyer, bior3.9, db5, db3, sym3 and haar. The new wavelet outperforms the rest

Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks

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

Study of thresholds to burning in surface grinding process

This work aims at finding out the threshold to burning in surface grinding process. Acoustic emission and electric power signals are acquired from an analog-digital converter and processed through algorithms in order to generate a control signal to inform the operator or interrupt the process in the case of burning occurrence. The thresholds that dictate the situation of burn and non-burn were studied as well as a comparison between the two parameters was carried out. In the experimental work one type of steel (ABNT-1045 annealed) and one type of grinding wheel referred to as TARGA model 3TG80.3-NV were employed. Copyright © 2005 by ABCM.

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.

Methodology for automatic selection of passes in surface grinding

The main purpose of this work is the development of computational tools in order to assist the on-line automatic detection of burn in the surface grinding process. Most of the parameters currently employed in the burning recognition (DPO, FKS, DPKS, DIFP, among others) do not incorporate routines for automatic selection of the grinding passes, therefore, requiring the user's interference for the choice of the active region. Several methods were employed in the passes extraction; however, those with the best results are presented in this article. Tests carried out in a surface-grinding machine have shown the success of the algorithms developed for pass extraction. Copyright © 2007 by ABCM.

Neural network approach for surface roughness prediction in surface grinding

Several systems are currently tested in order to obtain a feasible and safe method for automation and control of grinding process. This work aims to predict the surface roughness of the parts of SAE 1020 steel ground in a surface grinding machine. Acoustic emission and electrical power signals were acquired by a commercial data acquisition system. The former from a fixed sensor placed near the workpiece and the latter from the electric induction motor that drives the grinding wheel. Both signals were digitally processed through known statistics, which with the depth of cut composed three data sets implemented to the artificial neural networks. The neural network through its mathematical logical system interpreted the signals and successful predicted the workpiece roughness. The results from the neural networks were compared to the roughness values taken from the worpieces, showing high efficiency and applicability on monitoring and controlling the grinding process. Also, a comparison among the three data sets was carried out.

Monitoring the dressing operation in the grinding process

This work was based on a methodology of development and experimentation, and involved monitoring the dressing operation by processing the acoustic emission and electric power signals to detect the optimal dressing moment. Dressing tests were performed in a surface grinding machine with an aluminium grinding wheel. Dressing analysis software was developed and used to process the signals collected earlier in order to analyse not only the dressing parameters but also the software's ability to indicate the instant when the dressing operation could be concluded. Parameters used in the study of burn in grinding were implemented in order to ascertain if they would also prove efficient in monitoring dressing. A comparative study revealed that some parameters are capable of monitoring the dressing operation. It was possible to verify the parameters effectiveness that today are utilised in burning to monitor dressing as well as to create new parameters for monitoring this operation. Copyright © 2009, Inderscience Publishers.