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Automatic defects classification system using spectral analysis and neural networks
N DT Abstracts 56222
locations of the calibration points. The result of the simulation showed that a feedforward neural network can locate the subsurface AE source precisely in a two-dimensional plane by using this method.
Santos, J.; Perdigao, J.
Automatic defects classification system using spectral analysis and neural networks Ultrasonics International 93 Conference Proceedings, Vienna (Austria), 6-8 Jul. 1993. pp. 763-766. Butterworth-Heineman Ltd (1993) ISBN 0 750618779
55851 Yuki, H.; Homma, K. AE source waveform analysis by using a neural network Progress in Acoustic Emission VI Proceedings of the 1 lth International Acoustic Emission Symposium, Fukuoka (Japan), 26-29 Oct. 1992. pp. 235- 242. Edited by T. Kishi, K. Takahashi and M. Ohtsu. The Japanese Society for Non-Destructive Inspection ( 1993) Acoustic emission (AE) source waveform analysis using a neural
This paper deals with the ultrasonic signal analysis using the frequency spectrum and neural networks. As our aim with this work is to obtain information about defect characteristics, namely shape, size and physical nature, we describe an ultrasonic spectroscopy test system in order to perform the acquisition, sampling, digitizing and the signal processing by the Fast Fourier Transform I:FFT). The amplitude transfer function technique is used to extract infotmation of the defects. The inclusions were fabricated with different shapes, sizes and of different materials. To make the automatic classification of defects a neural network using the Back-Propagation training algorithm was implemented. 56201
network has been studied. AE signals emitted from an artificial AE source caused by pencil lead breaks were used. The signals were detected by a piezcelectric AE sensor and recorded in a transient converter. To determine the AE source waveform from a detected waveform, a layered network emulated by the computer program was used. Each unit of a layer in the network has been made to correspond each sampling point of the waveform. By using the network, the source waveform was determined as the output of the network when a detected waveform was provided to the input of the network. In order to learn the network, known source waveforms were required to present as teaching data in the network. These waveforms were calculated from displacement waveforms detected by a capacitive displacement transducer. If appropriate waveform data were selected for learning, analogous source waveforms could exactly be determined. The neural network method might be advantage to the source waveform analysis, in the sense to avoid instability of the numerical transfer function by the conventional analysis.
Gustafsson, G.; Stepinski, T.
Theory and adaptive algorithms related to the split spectrum technique for interference noise suppression Ultrasonics International 93 Conference Proceedings, Vienna (Austria), 6-8 Jul. 1993. pp. 355-358. Butterworth-Heinemann Ltd (1993) ISBN 0 750618779 The split spectrum technique for suppression of interference in ultrasonic testing is known to work well but is very sensitive to certain parameter values. In this contribution, it is shown how the polarity thresholding version of split spectrum can be formulated as a multilayer perceptron artificial neural network with binary neurons. Furthermore, relations between the split spectrum and well established techniques such as the short-time Fourier transfonn and the Wiener model of nonlinear dynamical systems are pointed out.
55744
min-max neural network based classification of underwater layered media due to attenuation effects Advances in Signal Processing for Nondestructive Evaluation of Materials, Quebec (Canada), 17-20 Aug. 1993. pp. 25 l-267. Edited by X.P.V. Maldague. Kluwer Academic Publishers (1994) ISBN O-7923-2765-9 A feed-forward fuzzy dual-connected neural network classitier that uses
56141 Sribar, R.; Sachse, U’. AE source characterization in lattice-type structures using smart signal processing Ultrasonics International 93 Conference Proceedings, Vienna (Austria), 6-8 Jul. 1993. pp. 221-226. Butterworth-Heineman Ltd. (1993) ISBN 0750618779 This paper describes results of an experimental investigation of acoustic
min-max amplitude ranges to define classes is designed and evaluated for underwater layered media recognition based on a computer simulation of synthetic data. A supervised fuzzy min-max learning rule updates the weights corresponding to the input data sets used for training. A simple nonlinear structure modeling underwater layered media response to acoustic inputs accounting for time delay in layers and the exponential decay of the output signals’ amplitude due to attenuation effects is employed for simulations used to test the classifier. Following the training stage, the classifier is tested with different test data sets. The results suggest that the application of fuzzy min- max neural networks in pattern recognition will enable automatic classification of the layered media with reasonable accuracy.
emission (AE) source characterization in terms of location and strength from strain gage signals detected on a two-dimensional frame- like structure. The signals are analyzed using two different smart signal processing algorithms. One is a feed forward neural network (FFNN) that was trained by a modified back propagation algorithm and the second is a linear system called an auto-associative processor (AAP). The common feature of these algorithms is the use of a set of pre-processed, measured prototype signals to develop a system memory. This memory is subsequently employed to process the detected signals to determine the location and strength of the AE source. 55992 c.
El-Hawary, F.; Setayeshi, S.
Fuzzy
Harrouche, K.; Derouiche, Z.; Rouvaen, J.M.; Delebarre,
55739
Maldague, X.P.V.
Advances in signal processing for nondestructive evaluation of materials Advances in Signal Processing for Nondestructive Evaluation of Materials, Quebec (Canada), 17-20 Aug. 1993. pp. 1-12. Edited by X.P.V. Maldague. Kluwer Academic Publishers (1994) ISBN O-7923-2765-9
Thickness measurement of multi-layered structures: a neural net approach IEEE 1993 Ultrasonics Symposium, Baltimore, Maryland (United States), 31 Oct. - 3 Nov. 1993. Vol. 2, pp. 749-752. Edited by M. Levy and B.R. McAvoy. IEEE (1993) ISSN 1051-0117 In our laboratory we got some experience about the high frequency characterisation of heterogeneous and multilayered structures. Several tools have been developed for this putposed. Our aim is to apply a neural net approach to this problem. Suitable neural net structures are designed for performing this task. Simulated echograms are used as inputs in the learning
The Paper is organized as follows. In Section 2 we describe the attributes of intelligent signal processors. In Section 3 we outline the important characteristics of neural networks that are basic to the construction of intelligent signal processors. In Section 4 we discuss time-frequency analysis and principal components analysis as important adjuncts to neural networks for preprocessing. In Section 5 we present some results demonstrating the application of these techniques to a difficult radar detection problem. Section 6 concludes the paper by presenting some final thoughts on the subject.
phase and then in the operating one. 55875 Miyazaki, A.; Niitsuma, H. Virtual calibration method for neural source location in subsurface AE measuremenl Progress in Acoustic Emission VI Proceedings of the 1 lth International Acoustic Emission Symposium, Fukuoka (Japan), 26-29 Oct. 1992. pp. 447- 454. Edited by T. Kishi. K. Takahashi and M. Ohtsu. The Japanese Society for Non-Destructive Inspection (1993) We have examined a feasibility of a feedforward back-propagation neural network for subsurface AE source location by a synthetic study. We describe a method to train a neural network by using calibration signals in this paper. Two-point triaxial measurement was supposed in this study and source location was made in vertical plane. It has been revealed that determination of a horizontal location is impossible by using calibration signals vertically aligned, because there is no variation on the horizontal
5
55688 Grabec, I. Prediction of chaotic AE signals by a neural network Progress in Acoustic Emission VI Proceedings of the 1lth International Acoustic Emission Symposium, Fukuoka (Japan), 26-29 Oct. 1992. pp. 17- 24. Edited by T. Kishi, K. Takahashi and M. Ohtsu. The Japanese Society for Non-Destructive InSpeCtiOn (1993) This article describes an adaptive information processing system capable of predicting chaotic acoustic emission signals. The system includes a neural network-like memory, a predictor, two shift regiskr~, ad a comparator. In the memory an internal model of an AE phenomenon is
locations of the calibration points. The result of the simulation showed that a feedforward neural network can locate the subsurface AE source precisely in a two-dimensional plane by using this method.
Santos, J.; Perdigao, J.
Automatic defects classification system using spectral analysis and neural networks Ultrasonics International 93 Conference Proceedings, Vienna (Austria), 6-8 Jul. 1993. pp. 763-766. Butterworth-Heineman Ltd (1993) ISBN 0 750618779
55851 Yuki, H.; Homma, K. AE source waveform analysis by using a neural network Progress in Acoustic Emission VI Proceedings of the 1 lth International Acoustic Emission Symposium, Fukuoka (Japan), 26-29 Oct. 1992. pp. 235- 242. Edited by T. Kishi, K. Takahashi and M. Ohtsu. The Japanese Society for Non-Destructive Inspection ( 1993) Acoustic emission (AE) source waveform analysis using a neural
This paper deals with the ultrasonic signal analysis using the frequency spectrum and neural networks. As our aim with this work is to obtain information about defect characteristics, namely shape, size and physical nature, we describe an ultrasonic spectroscopy test system in order to perform the acquisition, sampling, digitizing and the signal processing by the Fast Fourier Transform I:FFT). The amplitude transfer function technique is used to extract infotmation of the defects. The inclusions were fabricated with different shapes, sizes and of different materials. To make the automatic classification of defects a neural network using the Back-Propagation training algorithm was implemented. 56201
network has been studied. AE signals emitted from an artificial AE source caused by pencil lead breaks were used. The signals were detected by a piezcelectric AE sensor and recorded in a transient converter. To determine the AE source waveform from a detected waveform, a layered network emulated by the computer program was used. Each unit of a layer in the network has been made to correspond each sampling point of the waveform. By using the network, the source waveform was determined as the output of the network when a detected waveform was provided to the input of the network. In order to learn the network, known source waveforms were required to present as teaching data in the network. These waveforms were calculated from displacement waveforms detected by a capacitive displacement transducer. If appropriate waveform data were selected for learning, analogous source waveforms could exactly be determined. The neural network method might be advantage to the source waveform analysis, in the sense to avoid instability of the numerical transfer function by the conventional analysis.
Gustafsson, G.; Stepinski, T.
Theory and adaptive algorithms related to the split spectrum technique for interference noise suppression Ultrasonics International 93 Conference Proceedings, Vienna (Austria), 6-8 Jul. 1993. pp. 355-358. Butterworth-Heinemann Ltd (1993) ISBN 0 750618779 The split spectrum technique for suppression of interference in ultrasonic testing is known to work well but is very sensitive to certain parameter values. In this contribution, it is shown how the polarity thresholding version of split spectrum can be formulated as a multilayer perceptron artificial neural network with binary neurons. Furthermore, relations between the split spectrum and well established techniques such as the short-time Fourier transfonn and the Wiener model of nonlinear dynamical systems are pointed out.
55744
min-max neural network based classification of underwater layered media due to attenuation effects Advances in Signal Processing for Nondestructive Evaluation of Materials, Quebec (Canada), 17-20 Aug. 1993. pp. 25 l-267. Edited by X.P.V. Maldague. Kluwer Academic Publishers (1994) ISBN O-7923-2765-9 A feed-forward fuzzy dual-connected neural network classitier that uses
56141 Sribar, R.; Sachse, U’. AE source characterization in lattice-type structures using smart signal processing Ultrasonics International 93 Conference Proceedings, Vienna (Austria), 6-8 Jul. 1993. pp. 221-226. Butterworth-Heineman Ltd. (1993) ISBN 0750618779 This paper describes results of an experimental investigation of acoustic
min-max amplitude ranges to define classes is designed and evaluated for underwater layered media recognition based on a computer simulation of synthetic data. A supervised fuzzy min-max learning rule updates the weights corresponding to the input data sets used for training. A simple nonlinear structure modeling underwater layered media response to acoustic inputs accounting for time delay in layers and the exponential decay of the output signals’ amplitude due to attenuation effects is employed for simulations used to test the classifier. Following the training stage, the classifier is tested with different test data sets. The results suggest that the application of fuzzy min- max neural networks in pattern recognition will enable automatic classification of the layered media with reasonable accuracy.
emission (AE) source characterization in terms of location and strength from strain gage signals detected on a two-dimensional frame- like structure. The signals are analyzed using two different smart signal processing algorithms. One is a feed forward neural network (FFNN) that was trained by a modified back propagation algorithm and the second is a linear system called an auto-associative processor (AAP). The common feature of these algorithms is the use of a set of pre-processed, measured prototype signals to develop a system memory. This memory is subsequently employed to process the detected signals to determine the location and strength of the AE source. 55992 c.
El-Hawary, F.; Setayeshi, S.
Fuzzy
Harrouche, K.; Derouiche, Z.; Rouvaen, J.M.; Delebarre,
55739
Maldague, X.P.V.
Advances in signal processing for nondestructive evaluation of materials Advances in Signal Processing for Nondestructive Evaluation of Materials, Quebec (Canada), 17-20 Aug. 1993. pp. 1-12. Edited by X.P.V. Maldague. Kluwer Academic Publishers (1994) ISBN O-7923-2765-9
Thickness measurement of multi-layered structures: a neural net approach IEEE 1993 Ultrasonics Symposium, Baltimore, Maryland (United States), 31 Oct. - 3 Nov. 1993. Vol. 2, pp. 749-752. Edited by M. Levy and B.R. McAvoy. IEEE (1993) ISSN 1051-0117 In our laboratory we got some experience about the high frequency characterisation of heterogeneous and multilayered structures. Several tools have been developed for this putposed. Our aim is to apply a neural net approach to this problem. Suitable neural net structures are designed for performing this task. Simulated echograms are used as inputs in the learning
The Paper is organized as follows. In Section 2 we describe the attributes of intelligent signal processors. In Section 3 we outline the important characteristics of neural networks that are basic to the construction of intelligent signal processors. In Section 4 we discuss time-frequency analysis and principal components analysis as important adjuncts to neural networks for preprocessing. In Section 5 we present some results demonstrating the application of these techniques to a difficult radar detection problem. Section 6 concludes the paper by presenting some final thoughts on the subject.
phase and then in the operating one. 55875 Miyazaki, A.; Niitsuma, H. Virtual calibration method for neural source location in subsurface AE measuremenl Progress in Acoustic Emission VI Proceedings of the 1 lth International Acoustic Emission Symposium, Fukuoka (Japan), 26-29 Oct. 1992. pp. 447- 454. Edited by T. Kishi. K. Takahashi and M. Ohtsu. The Japanese Society for Non-Destructive Inspection (1993) We have examined a feasibility of a feedforward back-propagation neural network for subsurface AE source location by a synthetic study. We describe a method to train a neural network by using calibration signals in this paper. Two-point triaxial measurement was supposed in this study and source location was made in vertical plane. It has been revealed that determination of a horizontal location is impossible by using calibration signals vertically aligned, because there is no variation on the horizontal
5
55688 Grabec, I. Prediction of chaotic AE signals by a neural network Progress in Acoustic Emission VI Proceedings of the 1lth International Acoustic Emission Symposium, Fukuoka (Japan), 26-29 Oct. 1992. pp. 17- 24. Edited by T. Kishi, K. Takahashi and M. Ohtsu. The Japanese Society for Non-Destructive InSpeCtiOn (1993) This article describes an adaptive information processing system capable of predicting chaotic acoustic emission signals. The system includes a neural network-like memory, a predictor, two shift regiskr~, ad a comparator. In the memory an internal model of an AE phenomenon is