Piezoelectric composite film for acoustic emission detection

The continuous technological advances require materials with properties that conventional material cannot display. Material property combinations are being the focus to the development of composite materials, which are considered a multiphase material that exhibits properties of the constituent phases. One interesting material to be studied as sensing material is the composite made of ferroelectric ceramic and polymeric matrix as a two-phases composite material. In that case, the combinations properties intended are the high piezo and pyroelectric activities of the dense ceramic with the impact resistance, flexibility, formability and low densities of the polymer. Using the piezoelectric property of the composite film, it can be used to detect acoustic emission (AE), which is a transient elastic wave generated by sudden deformation in materials under stress. AE can be applied for evaluating the health of structures in a nondestructive way and without any lapse of time. The preliminary result indicates that the composite Pz34/PEEK can be used as sensing material for nondestructive evaluation. ©2009 IEEE.

Detection of cracks in scratching tests in ceramic materials through acoustic emission

The mechanisms of material removal and the interactions among scratches performed in ceramic materials were investigated using acoustic emission signals, and scanning electron microscopy, in scratching experiments. Several testing conditions were used to produce different types of removing mechanism on a glass as well as on a polycrystalline alumina sample composed by heterogeneous grain size. It is known that the material removing process on a polycrystalline ceramic involves intergranular microfracture and grain dislodgement, unlike the chipping produced by the extension of lateral cracks in non-granular materials, such as glass. Distinct settings for velocities, loads, and two types of diamond indenter were tested. The material removal was carried out by three different methods of scratching: single passes, repeated overlapping passes, and parallel scratches. As a general result, there was a clear relationship between the acoustic emission signals and the damage intensity occurred in the material removal. More specifically, there were differences in the acoustic emission signal levels in the scratches made on the alumina and on the glass owing to the material removal mechanisms associated with the structure of these materials. A gradual increase in the acoustic emission levels was observed when the number of repeated passes was increased as a result of the damage accumulation process followed by severe material removal. It was also noticed that the acoustic emission signals were capable of reflecting the interactions between two parallel scratches.

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.

Neural network detection of grinding burn from acoustic emission

An artificial neural network (ANN) approach is proposed for the detection of workpiece `burn', the undesirable change in metallurgical properties of the material produced by overly aggressive or otherwise inappropriate grinding. The grinding acoustic emission (AE) signals for 52100 bearing steel were collected and digested to extract feature vectors that appear to be suitable for ANN processing. Two feature vectors are represented: one concerning band power, kurtosis and skew; and the other autoregressive (AR) coefficients. The result (burn or no-burn) of the signals was identified on the basis of hardness and profile tests after grinding. The trained neural network works remarkably well for burn detection. Other signal-processing approaches are also discussed, and among them the constant false-alarm rate (CFAR) power law and the mean-value deviance (MVD) prove useful.

On the Acoustic Filtering of the Pipe and Sensor in a Buried Plastic Water Pipe and its Effect on Leak Detection: An Experimental Investigation

Acoustic techniques have been used for many years to find and locate leaks in buried water distribution systems. Hydrophones and accelerometers are typically used as sensors. Although geophones could be used as well, they are not generally used for leak detection. A simple acoustic model of the pipe and the sensors has been proposed previously by some of the authors of this paper, and their model was used to explain some of the features observed in measurements. However, simultaneous measurements of a leak using all three sensor-types in controlled conditions for plastic pipes has not been reported to-date and hence they have not yet been compared directly. This paper fills that gap in knowledge. A set of measurements was made on a bespoke buried plastic water distribution pipe test rig to validate the previously reported analytical model. There is qualitative agreement between the experimental results and the model predictions in terms of the differing filtering properties of the pipe-sensor systems. A quality measure for the data is also presented, which is the ratio of the bandwidth over which the analysis is carried out divided by the centre frequency of this bandwidth. Based on this metric, the accelerometer was found to be the best sensor to use for the test rig described in this paper. However, for a system in which the distance between the sensors is large or the attenuation factor of the system is high, then it would be advantageous to use hydrophones, even though they are invasive sensors.

Automatic detection of thermal damage in grinding process by artificial neural network

Esse trabalho tem por objetivo o desenvolvimento de um sistema inteligente para detecção da queima no processo de retificação tangencial plana através da utilização de uma rede neural perceptron multi camadas, treinada para generalizar o processo e, conseqüentemente, obter o limiar de queima. em geral, a ocorrência da queima no processo de retificação pode ser detectada pelos parâmetros DPO e FKS. Porém esses parâmetros não são eficientes nas condições de usinagem usadas nesse trabalho. Os sinais de emissão acústica e potência elétrica do motor de acionamento do rebolo são variáveis de entrada e a variável de saída é a ocorrência da queima. No trabalho experimental, foram empregados um tipo de aço (ABNT 1045 temperado) e um tipo de rebolo denominado TARGA, modelo ART 3TG80.3 NVHB.

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.

Pyroelectric composite film for X-ray intensity detection

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

Neurochemistry of the afferents to the rat cochlear root nucleus: Possible synaptic modulation of the acoustic startle

Afferents to the primary startle circuit are essential for the elicitation and modulation of the acoustic startle reflex (ASR). In the rat, cochlear root neurons (CRNs) comprise the first component of the acoustic startle circuit and play a crucial role in mediating the ASR. Nevertheless, the neurochemical pattern of their afferents remains unclear. To determine the distribution of excitatory and inhibitory inputs, we used confocal microscopy to analyze the immunostaining for vesicular glutamate and GABA transporter proteins (VGLUT1 and VGAT) on retrogradely labeled CRNs. We also used reverse transcription-polymerase chain reaction (RT-PCR) and immunohistochemistry to detect and localize specific neurotransmitter receptor subunits in the cochlear root. Our results show differential distributions of VGLUT1- and VGAT-immunoreactive endings around cell bodies and dendrites. The RT-PCR data showed a positive band for several ionotropic glutamate receptor subunits, M1-M5 muscarinic receptor subtypes, the glycine receptor alpha 1 subunit (GlyR alpha 1), GABA(A), GABA(B), and subunits of alpha 2 and beta-noradrenergic receptors. By immunohistochemistry, we confirmed that CRN cell bodies exhibit positive immunoreaction for the glutamate receptor (GluR) 3 and NR1 GluR subunits. Cell bodies and dendrites were also positive for M2 and M4, and GlyR alpha 1. Other subunits, such as GluR1 and GluR4 of the AMPA GluRs, were observed in glial cells neighboring unlabeled CRN cell bodies. We further confirmed the existence of nor-adrenergic afferents onto CRNs from the locus coeruleus by combining tyrosine hydroxylase immunohistochemistry and tract-tracing experiments. Our results provide valuable information toward understanding how CRNs might integrate excitatory and inhibitory inputs, and hence how they could elicit and modulate the ASR. (C) 2008 IBRO. Published by Elsevier Ltd. All rights reserved.

Detection of phenolic compounds using impedance spectroscopy measurements

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

Spatial pattern detection modeling of thrips (Thrips tabaci) on onion fields

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Well-conditioned robust observer for detection of family systems components

A new concept of fault detection and isolation using robust observation for systems with random noises is presented. The method selects the parameters from components that may fault during the process and constructs well conditioned robust observers, considering sensors faults. To isolate component failures via robust observation, a bank of detection observers is constructed, where each observer is only sensitive to one specified component failure while robust to all other component failures.

DETECTION OF CELL-PROLIFERATION IN TISSUE-SECTIONS

1. Cell proliferation is of interest since abnormal cell proliferation appears to be a precursor of tumorigenesis and also because the quantitative description of cell proliferation in tumors can be used to predict the biological behavior of a particular neoplasia.2. Them am several reliable methods of studying cell proliferation in tissues. One of the most important is the detection of the Ki67 defined antigen in frozen sections. The number of cells expressing Ki67 correlates with histological grades of tumors and can also be predictive of clinical outcome. The Ki67 can be localized in tissue sections using monoclonal antibodies in association with the immunoperoxidase technique.3. Proliferating cell nuclear antigen (PCNA) is a component of DNA polymerase-delta and is another important cell proliferation marker manifesting a striking increase in concentration during the S phase of the cell cycle. 19A2 and PC10 are two different monoclonal antibodies which can be employed to detect PCNA in paraffin-embedded tissues.4. Molecular biology has also been making a great contribution to the study of cell proliferation. The most recent innovation in tissue identification of proliferating cells is the use of in situ hybridization for the localization of histone H3 and/or H4 mRNA. H3 mRNA-positive cells appear to be present in basal cells of the skin and in crypt cells of the intestine which are sites with high proliferation rate.

Well-conditioned robust observer for detection of faulty systems components

A new concept of fault detection and isolation using robust observation for systems with random noises is presented. The method selects the parameters from components that may fault during the process and constructs well conditioned robust observers, considering sensors faults. To isolate component failures via robust observation, a bank of detection observers is constructed, where each observer is only sensitive to one specified component failure while robust to all other component failures.

Lead zirconate titanate/polyurethane (PZT/PU) composite for acoustic emission sensors

Piezoelectric composite, made from ferroelectric ceramic lead zirconate titanate (PZT) and vegetable based polyurethane (PU) polymer, was doped with a semiconductor filler, graphite. The resulting composite (PZT/C/PU) with 49/1/50- vol. % composition could be poled at lower field and shorter time due to the increased conductivity of the polymer phase following the introduction of graphite. The PZT/C/PU composite showed higher pyroelectric coefficient in comparison with the undoped PZT/PU composite with 50/50-vol. % composition. Also, the PZT/C/PU composite has shown the ability to detect both extensional and flexural modes of simulated acoustic emission (AE) at a distance up to 8.0 m from the source, thus indicating that it may be used for detection of structural damages.