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The application of artificial neural network to defect characterization in eddy current NDT
N DT Abstracts Owechko, Y.; Softer, B.H. Holographic neurocomputer utilizing laser-diode light source 60949
Yah, W.; Upadhyaya, B.R. An integrated signal processing and neural networks system for steam generator tubing diagnostics using eddy current inspection 60734
Proceedings of an International Conference on Optical Implementation of Information Processing, San Diego, California (United States), 10-11 Jul. 1995. pp. 12-19. Edited by B. Javidi and J.L. Homer. SPIE (1995) ISBN 0819419249 We describe a laser diode-based optoelectronic implementation of artificial neural networks which utilizes real-time holography in photorefractive crystals. The use of a laser diode light source reduce the system size and power requirements. The holographic material is rhodiumdoped BaTiOsub(3) which has enhanced sensitivity at the laser- diode wavelength of 830 nm. A balanced coherent-detection method is used to represent bipolar optical neurons and weights. The structure of the neural network is programmable and we have implemented a variety of neural networks including backpropagation and Kohonen-style self- organizing maps with up to 10,000 neurons and performance of up to 10sup(8) weights processed per second during learning and readout.
Annals of Nuclear Energy, Vol. 23, No. 10, pp. 813-825 (1996) The primary purpose of this research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive evaluation (NDE) dam. The focus of the research was to develop and test various data compression methods (for eddy current data) and the performance of different neural network paradigms for defect classification and defect parameter estimation. Feedforward, fullyconnected neural networks, that use the back-propagation algorithm for network training, were implemented for defect classification and defect parameter estimation using a modular network architecture.
Grimaldi, V. Using unsupervised neural networks for eddy currents signature discrimination: a prospective study (In French) 60730
Matsumoto, Y.; Komatsu, H.; Badics, Z.; Aoki, K.; Nakayasu, F.; Hashimoto, M.; Miya, K. Automatic analysis of eddy current data using neural network 60919
Electricite de France, Clamart (France), EDF-95-NB-00029, 49 pp. (Feb. 1994) This report describes an application of unsupervised neural networks for eddy current Non Destructive Testing (NDT) inspection of steam generator tubes. This is an original neural approach to defect detection, characterized by two aspects: diagnosis is expressed in architectural terms and the potential advantages of using unsupervised neural techniques are systematically discussed. We present the framework of the Kohenen self organizing maps within the proposed diagnosis architecture.
Proceedings of a Steam Generator and Heat Exchanger Conference, Toronto (Canada), Jun. 1994, pp. 2.35-2.45. Canadian Nuclear Society (1995). Vol. I. ISBN 0919784402 We performed investigation the capability of neural network to identify maximum ECT signal among mixed signals at tube support plate region. Secondly we tested the capability of neural network to evaluate the defect quantitatively. Thirdly we compared the quantitative detectability of defects between neural network and ECT data analyst.
60728 Georgel, B.; Zorgati, R. Extraction: a system for automatic eddy current diagnosis of steam generator tubes in nuclear power plants (In French) Electricite de France, Clamart (France), EDF-95-NB-00024, 9 pp. (1994) Improving speed and quality of Eddy Current non-destructive testing of steam generator tubes leads to automatize all processes that contribute to diagnosis. This paper describes how we use signal processing, pattern recognition and artificial intelligence to build a software package that is able to automatically provide an efficient diagnosis.
60814 Koh, C.S.; Mohammed, O.A.; Jung, H-K.; Hahn, S-y. The application of artificial neural network to defect characterization in eddy current NDT Proceedings of the International ISEM Symposium on Advanced Computational and Design Techniques in Applied Electromagnetic Systems, Seoul (Korea), 22-24 Jun. 1994, pp. 161-164. Edited by Song-yop Hahn, Elsevier (1995) ISBN 0444821392 An Artificial Neural Network (ANN) is applied to find out the defect in the conducting material. The ANN is trained with data set (defect parameters : defect signal output), where the defect signal is Lissajou's figure and it is simulated using axisymmetric finite element method.
Yan, W.; Upadhyaya, B.R. An integrated signal processing and neural networks system for steam generation tubing diagnostics using eddy current inspection 60724
60810 Booth, C.; McDonald, J.R.; A resi, R. The use of artificial neural networks for the estimation and classification of vibration behaviour in power transformers
Annals of Nuclear Energy, Vol. 23, No. 10, pp. 813-825 (Jul. 1996) The primary purpose of this research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive evaluation (NDE) data. These data were used to study the performance of artificial neural networks for defect type classification and for estimating defect parameters. Most of the study was made using the Neural/Works Professional II/Plus software. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods.
Proceedings of the American Power Conference, Spons. Illinois Institute of Technology, Vol. 57-II, pp. 1132-1137 (1995) This paper reports on work undertaken using Artificial Neutral Networks (ANNs) to both estimate and classify the vibrational behaviour of a power transformer. The inputs to the ANNs consist of data which is typically available on-line such as voltage, current and temperature measurements. Extensive test data from an actual power transformer, fitted with the necessary instrumentation, has been used to train the ANNsa and test their performance. This project may culminate in the incorporation of this methodology in an integrated transformer condition monitoring system.
Kim, T.R.; Jeong, S.H.; Park, J.H.; Park, J.S. A study on the fault diagnostic techniques for reactor internal structures using neutron noise analysis 6071 I
Kotani, M.; Matsumoto, H. Application of neural networks to acoustic diagnosis (In Japanese) 60808
Korea Atomic Energy Research Institute, Daeduk (Republic of Korea), KAERI-RR-1386/94, 163 pp. (Aug. 1994) (In Korean) The objectives of this study are to establish fault diagnostic and TS(thermal shield), and to develop a data acquisition and signal processing software system. An analysis technique for the reactor internal vibration using the reactor noise was proposed and the reactor noise signals (ex-core neutron and acceleration), the dynamic characteristics of Ulehin-I reactor internals were obtained, and compared with those of Tricastin-1. A PC-based expert system for reactor internals fault diagnosis is developed, which included data acquisition, signal processing, feature extraction function, and represented diagnostic knowledge by the IF-THEN rule.
Journal of JSNDI, Vol. 45, No. 2, pp. 107-113 (1996)
Baillie, D.C.; Mathew, J. Nonlinear model-based fault diagnosis of bearings
60795
Proceedings of an International Conference on Condition Monitoring, Swansea (United Kingdom), 21-25 Mar. 1994, pp. 241-252. Edited by M.H. Jones. Pineridge Press (1994). ISBN 0906674832 This paper reviews the concept of model-based fault diagnosis for online vibration condition monitoring of steady-speed rotating machinery. A model-based diagnostic system built from a number of nonlinear models representing a set of rolling element bearing faults is described. Multi-layer artificial neural networks were utilised for the construction of the nonlinear autoregressive models for each class of the time domain vibration signals. It is shown that the model-based system provides an alternative machinery fault diagnosis technique that is ideally suited to applications where real-time processing of limited amounts of data is required.
Shyur, H.J.; Luxhoj, J.T.; Williams, T.P. Using neural networks to predict component inspection requirements for aging a i r c r a f t 60631
Computers and Industrial Engineering, Vol. 30, No. 2, pp. 257-267 (1996) An artificial neural network model is created to predict the number of SDRs that could be expected by part location using sample data from the
328
Yah, W.; Upadhyaya, B.R. An integrated signal processing and neural networks system for steam generator tubing diagnostics using eddy current inspection 60734
Proceedings of an International Conference on Optical Implementation of Information Processing, San Diego, California (United States), 10-11 Jul. 1995. pp. 12-19. Edited by B. Javidi and J.L. Homer. SPIE (1995) ISBN 0819419249 We describe a laser diode-based optoelectronic implementation of artificial neural networks which utilizes real-time holography in photorefractive crystals. The use of a laser diode light source reduce the system size and power requirements. The holographic material is rhodiumdoped BaTiOsub(3) which has enhanced sensitivity at the laser- diode wavelength of 830 nm. A balanced coherent-detection method is used to represent bipolar optical neurons and weights. The structure of the neural network is programmable and we have implemented a variety of neural networks including backpropagation and Kohonen-style self- organizing maps with up to 10,000 neurons and performance of up to 10sup(8) weights processed per second during learning and readout.
Annals of Nuclear Energy, Vol. 23, No. 10, pp. 813-825 (1996) The primary purpose of this research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive evaluation (NDE) dam. The focus of the research was to develop and test various data compression methods (for eddy current data) and the performance of different neural network paradigms for defect classification and defect parameter estimation. Feedforward, fullyconnected neural networks, that use the back-propagation algorithm for network training, were implemented for defect classification and defect parameter estimation using a modular network architecture.
Grimaldi, V. Using unsupervised neural networks for eddy currents signature discrimination: a prospective study (In French) 60730
Matsumoto, Y.; Komatsu, H.; Badics, Z.; Aoki, K.; Nakayasu, F.; Hashimoto, M.; Miya, K. Automatic analysis of eddy current data using neural network 60919
Electricite de France, Clamart (France), EDF-95-NB-00029, 49 pp. (Feb. 1994) This report describes an application of unsupervised neural networks for eddy current Non Destructive Testing (NDT) inspection of steam generator tubes. This is an original neural approach to defect detection, characterized by two aspects: diagnosis is expressed in architectural terms and the potential advantages of using unsupervised neural techniques are systematically discussed. We present the framework of the Kohenen self organizing maps within the proposed diagnosis architecture.
Proceedings of a Steam Generator and Heat Exchanger Conference, Toronto (Canada), Jun. 1994, pp. 2.35-2.45. Canadian Nuclear Society (1995). Vol. I. ISBN 0919784402 We performed investigation the capability of neural network to identify maximum ECT signal among mixed signals at tube support plate region. Secondly we tested the capability of neural network to evaluate the defect quantitatively. Thirdly we compared the quantitative detectability of defects between neural network and ECT data analyst.
60728 Georgel, B.; Zorgati, R. Extraction: a system for automatic eddy current diagnosis of steam generator tubes in nuclear power plants (In French) Electricite de France, Clamart (France), EDF-95-NB-00024, 9 pp. (1994) Improving speed and quality of Eddy Current non-destructive testing of steam generator tubes leads to automatize all processes that contribute to diagnosis. This paper describes how we use signal processing, pattern recognition and artificial intelligence to build a software package that is able to automatically provide an efficient diagnosis.
60814 Koh, C.S.; Mohammed, O.A.; Jung, H-K.; Hahn, S-y. The application of artificial neural network to defect characterization in eddy current NDT Proceedings of the International ISEM Symposium on Advanced Computational and Design Techniques in Applied Electromagnetic Systems, Seoul (Korea), 22-24 Jun. 1994, pp. 161-164. Edited by Song-yop Hahn, Elsevier (1995) ISBN 0444821392 An Artificial Neural Network (ANN) is applied to find out the defect in the conducting material. The ANN is trained with data set (defect parameters : defect signal output), where the defect signal is Lissajou's figure and it is simulated using axisymmetric finite element method.
Yan, W.; Upadhyaya, B.R. An integrated signal processing and neural networks system for steam generation tubing diagnostics using eddy current inspection 60724
60810 Booth, C.; McDonald, J.R.; A resi, R. The use of artificial neural networks for the estimation and classification of vibration behaviour in power transformers
Annals of Nuclear Energy, Vol. 23, No. 10, pp. 813-825 (Jul. 1996) The primary purpose of this research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive evaluation (NDE) data. These data were used to study the performance of artificial neural networks for defect type classification and for estimating defect parameters. Most of the study was made using the Neural/Works Professional II/Plus software. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods.
Proceedings of the American Power Conference, Spons. Illinois Institute of Technology, Vol. 57-II, pp. 1132-1137 (1995) This paper reports on work undertaken using Artificial Neutral Networks (ANNs) to both estimate and classify the vibrational behaviour of a power transformer. The inputs to the ANNs consist of data which is typically available on-line such as voltage, current and temperature measurements. Extensive test data from an actual power transformer, fitted with the necessary instrumentation, has been used to train the ANNsa and test their performance. This project may culminate in the incorporation of this methodology in an integrated transformer condition monitoring system.
Kim, T.R.; Jeong, S.H.; Park, J.H.; Park, J.S. A study on the fault diagnostic techniques for reactor internal structures using neutron noise analysis 6071 I
Kotani, M.; Matsumoto, H. Application of neural networks to acoustic diagnosis (In Japanese) 60808
Korea Atomic Energy Research Institute, Daeduk (Republic of Korea), KAERI-RR-1386/94, 163 pp. (Aug. 1994) (In Korean) The objectives of this study are to establish fault diagnostic and TS(thermal shield), and to develop a data acquisition and signal processing software system. An analysis technique for the reactor internal vibration using the reactor noise was proposed and the reactor noise signals (ex-core neutron and acceleration), the dynamic characteristics of Ulehin-I reactor internals were obtained, and compared with those of Tricastin-1. A PC-based expert system for reactor internals fault diagnosis is developed, which included data acquisition, signal processing, feature extraction function, and represented diagnostic knowledge by the IF-THEN rule.
Journal of JSNDI, Vol. 45, No. 2, pp. 107-113 (1996)
Baillie, D.C.; Mathew, J. Nonlinear model-based fault diagnosis of bearings
60795
Proceedings of an International Conference on Condition Monitoring, Swansea (United Kingdom), 21-25 Mar. 1994, pp. 241-252. Edited by M.H. Jones. Pineridge Press (1994). ISBN 0906674832 This paper reviews the concept of model-based fault diagnosis for online vibration condition monitoring of steady-speed rotating machinery. A model-based diagnostic system built from a number of nonlinear models representing a set of rolling element bearing faults is described. Multi-layer artificial neural networks were utilised for the construction of the nonlinear autoregressive models for each class of the time domain vibration signals. It is shown that the model-based system provides an alternative machinery fault diagnosis technique that is ideally suited to applications where real-time processing of limited amounts of data is required.
Shyur, H.J.; Luxhoj, J.T.; Williams, T.P. Using neural networks to predict component inspection requirements for aging a i r c r a f t 60631
Computers and Industrial Engineering, Vol. 30, No. 2, pp. 257-267 (1996) An artificial neural network model is created to predict the number of SDRs that could be expected by part location using sample data from the
328