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Material classification through neural networks
N DT Abstracts Anisimov, S.D.; Vinogradova, LY. Discriminant analysis for multiparameter electromagnetic inspection 59700
concrete structures was developed in the late 1980's. Because of the overwhelming amount of information in ultrasonic pulse echo (UPE) signals from concrete, learning to interpret raw data can be confusing. Although a human has the ability to recognize and calculate the complex signal information it can take months or years to master interpretation. The application of digital signal processing (DSP) algorithms for computerized signal interpretation can help reduce these problems. Computer processing of signals is also needed so that there is less dependency on highlyexperienced personnel and more objective diagnostic decisions can be made. This paper explores several DSP techniques aimed at computer interpretation. Three techniques were investigated: split-spectrum processing (SSP), ray-based modeling (RAM), and artificial neural networks (ANN).
Russian Journal of Nondestructive Testing, Vol. 30, No. 11, pp. 847- 850 (Nov. 1994) A method of discriminant analysis for measurement and identification of multiparameter nondestructive testing is considered. Its advantages over regression analysis in the area of nondestructive testing are shown. A personal computer program for conducting statistical discriminant analysis for the purpose of calculation of the training parameters of nondestructive testing equipment is proposed.
Laursen,P.; Atherton, D.L.; Development of small diameter in-line MFL inspection tools: a technical challenge 59683
Tang, M.X.; Dharmavasan, S.; Peers, S.M.C. Use of knowledge based systems for r a t i o n a l reliability analysis based inspection and maintenance planning for offshore 59560
Proceedings of the 4th (1994) International Offshore and Polar Engineering Conference, Osaka (Japan), I 0-15 Apr. 1994, Vol. IV, pp. 425-430. Edited by Y. Ueda, J.F. Dos Santos, I. Langen, Y. Tomita and K. Waagaard. ISOPE (1994) ISBN 1-880653-14- I. Magnetic Flux Leakage is the most cost effective inspection method currently used. There is now a significant demand for tools with diameters from 100 to 305 mm. The tool may also have to negotiate bends with radii as small as 1.5 pipe diameters, so trains with up to ten sections coupled with flexible links have been developed. Custom state-of-the-art solid state electronics is used for sensing devices, data acquisition and storage systems, together with high performance magnetic materials and the latest, most powerful magnets.
structures Proceedings of the 4th (1994) International Offshore and Polar Engineering Conference, Osaka (Japan), l 0-15 Apr. 1994. Vol, IV, pp. 514-523. Edited by Y. Ueda, J.F. Dos Santos, I. Langen, Y. Tomita and K. Waagaard. ISOPE (1994) ISBN 1-880653-14-1. A prototype system being developed for integrating reliability based analysis with other constraints for inspection scheduling will be described. in addition, the scheduling model and the algorithms to carry out the scheduling will be explained. Furthermore, implementation details are also given.
Newell, K. Data analysis of remote field data utilizing vector display techniques 59462
Roy, A.; Barat, P.; De, S.K. Material classification through neural networks 59611
Ultrasonics, Vol. 33, No. 3, pp. 175-180 (May 1995) Ultrasonic back wall echoes received from copper and aluminium plates of varying thicknesses are classified through neural network analysis for in situ material identification. To reduce the effect of thickness variation on the time domain signals, and the dimensionality, the Karhunen-Loeve transform was explored. Enormous data compression was achieved; however, the dimensionality of the reduced space was not constant and increased with the incorporation of the new ultrasonic signals from samples of different thicknesses. The power spectra in the frequency domain, on the other hand, was concentrated in the initial few discrete frequency components independent of thickness. A multi- layered feed-forward artificial neural network was trained by the frequency domain signals of the two classes, it was found that the performance of the learned network was quite reliable on the test samples even in cases where the thickness of the test sample is different from the learned samples.
ASNT 1994 Fall Conference and Quality Testing Show, Atlanta Georgia (United States), 19-23 Sep. 1994. pp. 122-125. ASNT (1994) ISBN 1- 57117-002-2 Recent technological advances now provide the ability to apply a more standardized approach to remote field data acquisition and interpretation. This technology reduces the reliance upon technician experience, historical operation and other complimentary NDE methods. This technique produces two dimensional signature patterns containing phase and amplitude information which are presented on a vector display. Once these signature pattems are learned, technicians with a similar training basis can interpret the data consistently and accurately. This technique also utilizes multiple frequencies and test modes to confirm and assess damage in a more comprehensive manner. 59391 Walker,J.; Workman, G.; Martin, N. Flaw detection in powder metallurgy formed impact cages by a neural network analysis of acousto-ultrasonic data
Hay, D.R.; Brassard, M.; Matthews, J.R.; Garneau, S.; Morchat, R. Enhancement of submarine pressure hull steel ultrasonic inspection using imaging and artificial intelligence 59603
ASNT 1994 Fall Conference and Quality Testing Show, Atlanta Georgia (United States), 19-23 Sep. 1994. pp. 99-101. ASNT (1994) ISBN 1-57117- 002-2 An acousto-ultrasonic (AU) flaw detection system is under development that will provide near real-time quality assurance for the production of powder formed impact cages. The region of interest for this work is the geometric discontinuity between the cages' head and body where, during the manufacturing process, cracks may develop. The procedures described in this paper illustrate an AU approach for detecting the presence of the defects through the variations in the energy content of the measured acoustic response, produced from various pulser-receiver combinations. The energy is computed from the power spectral density curves of the AU data and modeled by a back propagation neural network trained to provide a go-no go system for the quality assurance of the impact cages.
Nondestructive Evaluation of Aging Maritime Applications, Oakland, California (United States), 8 Jun. 1995. pp. 79-90. Edited by R.B. Mignogna. SPIE. Vol. 2459 (1995) ISBN 0-8194-1812-9. An automated ultrasonic inspection and data collection system, APHIUS (Automated Pressure Hull intelligent Ultrasonic System), incorporates hardware and software developments to meet specific requirements for the maritime vessels. Housed within a hardened portable computer chassis, instrumentation for digital ultrasonic data acquisition and transducer position measurement provide new capabilities that meet more demanding requirements for inspection of the aging submarine fleet. Digital data acquisition enables a number of new important capabilities including archiving of the complete inspection session, interpretation assistance through imaging, and automated interpretation using artificial intelligence methods. With this new reliable inspection system, in conjunction with a complementary study of the significance of real defect type and location, comprehensive new criteria can be generated which will eliminate unnecessary defect removal. As a consequence, cost savings will be realized through shortened submarine refit schedules.
Terada,A.; Kataoka, S.; Takano, J. Diagnostic system for ultrasonic examination utilizing neural networks 59357
Proceedings of the 13th International Conference on NDE in the Nuclear and Pressure Vessel Industries, Kyoto (Japan), 22-25 May 1995. pp. 147- ! 52. ASM International (1995) ISBN 0-87170-548-6 In order to increase the reliability of the system, a series of UT echoes around a peak echo which contains the important information for human inspectors. The network consists of 3 layers. The modified backpropagation method is employed for a learning algorithm. The preprocessing arranges 5 echoes and makes an input pattern for the system. Through several examinations, the optimum reprocessing for an input pattern is settled. By the improved system, the probability of correct call in the evaluation have increased up to about 90%, although the system shows a little dependence on input data.
Haskins, R.; Alexander, A.M. Computer interpretation of ultrasonic pulse-echo signs for concrete dams 59602
Nondestructive Evaluation of Aging Structures and Dams, Oakland, California (United States), 7-8 J un. 1995. pp. 182-194. Edited by S. Nazarian and L.D. Olsen. SPIE. Vot. 2457 (1995) ISBN 0-81941810-2. An ultrasonic pulse echo (UPE) system for the non-destructive testing of
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concrete structures was developed in the late 1980's. Because of the overwhelming amount of information in ultrasonic pulse echo (UPE) signals from concrete, learning to interpret raw data can be confusing. Although a human has the ability to recognize and calculate the complex signal information it can take months or years to master interpretation. The application of digital signal processing (DSP) algorithms for computerized signal interpretation can help reduce these problems. Computer processing of signals is also needed so that there is less dependency on highlyexperienced personnel and more objective diagnostic decisions can be made. This paper explores several DSP techniques aimed at computer interpretation. Three techniques were investigated: split-spectrum processing (SSP), ray-based modeling (RAM), and artificial neural networks (ANN).
Russian Journal of Nondestructive Testing, Vol. 30, No. 11, pp. 847- 850 (Nov. 1994) A method of discriminant analysis for measurement and identification of multiparameter nondestructive testing is considered. Its advantages over regression analysis in the area of nondestructive testing are shown. A personal computer program for conducting statistical discriminant analysis for the purpose of calculation of the training parameters of nondestructive testing equipment is proposed.
Laursen,P.; Atherton, D.L.; Development of small diameter in-line MFL inspection tools: a technical challenge 59683
Tang, M.X.; Dharmavasan, S.; Peers, S.M.C. Use of knowledge based systems for r a t i o n a l reliability analysis based inspection and maintenance planning for offshore 59560
Proceedings of the 4th (1994) International Offshore and Polar Engineering Conference, Osaka (Japan), I 0-15 Apr. 1994, Vol. IV, pp. 425-430. Edited by Y. Ueda, J.F. Dos Santos, I. Langen, Y. Tomita and K. Waagaard. ISOPE (1994) ISBN 1-880653-14- I. Magnetic Flux Leakage is the most cost effective inspection method currently used. There is now a significant demand for tools with diameters from 100 to 305 mm. The tool may also have to negotiate bends with radii as small as 1.5 pipe diameters, so trains with up to ten sections coupled with flexible links have been developed. Custom state-of-the-art solid state electronics is used for sensing devices, data acquisition and storage systems, together with high performance magnetic materials and the latest, most powerful magnets.
structures Proceedings of the 4th (1994) International Offshore and Polar Engineering Conference, Osaka (Japan), l 0-15 Apr. 1994. Vol, IV, pp. 514-523. Edited by Y. Ueda, J.F. Dos Santos, I. Langen, Y. Tomita and K. Waagaard. ISOPE (1994) ISBN 1-880653-14-1. A prototype system being developed for integrating reliability based analysis with other constraints for inspection scheduling will be described. in addition, the scheduling model and the algorithms to carry out the scheduling will be explained. Furthermore, implementation details are also given.
Newell, K. Data analysis of remote field data utilizing vector display techniques 59462
Roy, A.; Barat, P.; De, S.K. Material classification through neural networks 59611
Ultrasonics, Vol. 33, No. 3, pp. 175-180 (May 1995) Ultrasonic back wall echoes received from copper and aluminium plates of varying thicknesses are classified through neural network analysis for in situ material identification. To reduce the effect of thickness variation on the time domain signals, and the dimensionality, the Karhunen-Loeve transform was explored. Enormous data compression was achieved; however, the dimensionality of the reduced space was not constant and increased with the incorporation of the new ultrasonic signals from samples of different thicknesses. The power spectra in the frequency domain, on the other hand, was concentrated in the initial few discrete frequency components independent of thickness. A multi- layered feed-forward artificial neural network was trained by the frequency domain signals of the two classes, it was found that the performance of the learned network was quite reliable on the test samples even in cases where the thickness of the test sample is different from the learned samples.
ASNT 1994 Fall Conference and Quality Testing Show, Atlanta Georgia (United States), 19-23 Sep. 1994. pp. 122-125. ASNT (1994) ISBN 1- 57117-002-2 Recent technological advances now provide the ability to apply a more standardized approach to remote field data acquisition and interpretation. This technology reduces the reliance upon technician experience, historical operation and other complimentary NDE methods. This technique produces two dimensional signature patterns containing phase and amplitude information which are presented on a vector display. Once these signature pattems are learned, technicians with a similar training basis can interpret the data consistently and accurately. This technique also utilizes multiple frequencies and test modes to confirm and assess damage in a more comprehensive manner. 59391 Walker,J.; Workman, G.; Martin, N. Flaw detection in powder metallurgy formed impact cages by a neural network analysis of acousto-ultrasonic data
Hay, D.R.; Brassard, M.; Matthews, J.R.; Garneau, S.; Morchat, R. Enhancement of submarine pressure hull steel ultrasonic inspection using imaging and artificial intelligence 59603
ASNT 1994 Fall Conference and Quality Testing Show, Atlanta Georgia (United States), 19-23 Sep. 1994. pp. 99-101. ASNT (1994) ISBN 1-57117- 002-2 An acousto-ultrasonic (AU) flaw detection system is under development that will provide near real-time quality assurance for the production of powder formed impact cages. The region of interest for this work is the geometric discontinuity between the cages' head and body where, during the manufacturing process, cracks may develop. The procedures described in this paper illustrate an AU approach for detecting the presence of the defects through the variations in the energy content of the measured acoustic response, produced from various pulser-receiver combinations. The energy is computed from the power spectral density curves of the AU data and modeled by a back propagation neural network trained to provide a go-no go system for the quality assurance of the impact cages.
Nondestructive Evaluation of Aging Maritime Applications, Oakland, California (United States), 8 Jun. 1995. pp. 79-90. Edited by R.B. Mignogna. SPIE. Vol. 2459 (1995) ISBN 0-8194-1812-9. An automated ultrasonic inspection and data collection system, APHIUS (Automated Pressure Hull intelligent Ultrasonic System), incorporates hardware and software developments to meet specific requirements for the maritime vessels. Housed within a hardened portable computer chassis, instrumentation for digital ultrasonic data acquisition and transducer position measurement provide new capabilities that meet more demanding requirements for inspection of the aging submarine fleet. Digital data acquisition enables a number of new important capabilities including archiving of the complete inspection session, interpretation assistance through imaging, and automated interpretation using artificial intelligence methods. With this new reliable inspection system, in conjunction with a complementary study of the significance of real defect type and location, comprehensive new criteria can be generated which will eliminate unnecessary defect removal. As a consequence, cost savings will be realized through shortened submarine refit schedules.
Terada,A.; Kataoka, S.; Takano, J. Diagnostic system for ultrasonic examination utilizing neural networks 59357
Proceedings of the 13th International Conference on NDE in the Nuclear and Pressure Vessel Industries, Kyoto (Japan), 22-25 May 1995. pp. 147- ! 52. ASM International (1995) ISBN 0-87170-548-6 In order to increase the reliability of the system, a series of UT echoes around a peak echo which contains the important information for human inspectors. The network consists of 3 layers. The modified backpropagation method is employed for a learning algorithm. The preprocessing arranges 5 echoes and makes an input pattern for the system. Through several examinations, the optimum reprocessing for an input pattern is settled. By the improved system, the probability of correct call in the evaluation have increased up to about 90%, although the system shows a little dependence on input data.
Haskins, R.; Alexander, A.M. Computer interpretation of ultrasonic pulse-echo signs for concrete dams 59602
Nondestructive Evaluation of Aging Structures and Dams, Oakland, California (United States), 7-8 J un. 1995. pp. 182-194. Edited by S. Nazarian and L.D. Olsen. SPIE. Vot. 2457 (1995) ISBN 0-81941810-2. An ultrasonic pulse echo (UPE) system for the non-destructive testing of
332