- Volume 30 Issue 5 Serial No. 248
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Stress Classification Using Artificial Neural Networks and Fatigue Life Assessment
인공신경망을 이용한 계측응력 분류 및 피로수명 평가
- Published : 2006.05.01
The design of major industrial facilities for the prevention of fatigue failure is customarily done by defining a set of transients and performing a calculation of cumulative usage factor. However, sometimes, the inherent conservatism or lack of details as well as unanticipated transients in old plant may cause maintenance problems. Even though several famous on-line monitoring and diagnosis systems have been developed world-widely, in this paper, a new system fur fatigue monitoring and life evaluation of crane is proposed to reduce customizing effort and purchasing cost. With regard to the system, at first, comprehensive operating transient data has been acquired at critical locations of crane. The real-time data were classified, by using adaptive resonance theory that is one of typical artificial neural network, into representative stress groups. Then the each classified stress pattern was mapped to calculated cumulative usage factor in accordance with ASME procedure. Thereby, promising results were obtained fur the crane and it is believed that the developed system can be applicable to other major facilities extensively.
Adaptive Resonance Theory;Artificial Neural Networks;Cumulative Usage Factor;Stress Classification
- Jung, D.K., et al., 2000, 'The Development of Boiler Life Monitoring System,' Korea Heavy Industries Co.
- Ricardella, P.C., et al., 1988, 'FatiguePro: On-Line Fatigue Usage Transient Monitoring System,' EPRI NP-5835M
- Aufort, P., et al., 1991, 'On-line Fatiguemeter: A Large Experiment in French Nuclear Plants,' Nuclear Engineering and Design, Vol. 129, pp. 177-184 https://doi.org/10.1016/0029-5493(91)90092-V
- Huttner, C. and Kroiss, G., 1993, 'Operational Monitoring of the Long-Time Characteristics of Components - Computing Algorithm for Determining the Current Degree of Fatigue,' SMiRT-12, pp. 301-306
- Yoo, B., Goo, K.H. and Lee, H.Y., 1993, 'Development of K-FAMS Fatigue Damage Monitoring System,' 1st Piping Integrity Assessment Technology Workshop, pp. 89-106
- Jin, T.E., et al., 1996, 'A Study on the Development of Diagnostic Monitoring System for Life Assessment of Nuclear Power Plants,' Korea Power Engineering Company, INC.
- Lee Kang Yong, Kim Jong Sung, et al. 1997, 'Residual Life Evaluation of Pressurizer Surge Line Nozzle in Nuclear Plant,' Transaction of the KSME Spring Annual Meeting (B), pp. 727-732
- Yoo, T.K., 2002, 'A Study on Fatigue Life Evaluation for Steel-making Ladle Crane,' Master's Thesis, Sungkyunkwan Univ.
- McClellan, J.H., Schafer, R.W. and Yoder, M.A., 1997, 'DSP First: A Multimedia Approach,' Prentice Hall, p. 98
- McClellan, J.H., Schafer, R.W. and Yoder, M.A., 1997, 'DSP First: A Multimedia Approach,' Prentice Hall, pp. 96-97
- Silberschatz, A., Korth, H.F. and Sudarshan, S., 2002, 'Database System Concepts 4th Edition,' McGrawHill, New York, pp. 3-4
- Dally, J.W. and Riley, W.F., 1991, 'Experimental Stress Analysis,' 3rd Edition, McGraw-Hill, pp. 313-315
- Carpenter, G. A. and Grossberg, S., 1998, 'Adaptive Resonance Theory,' MIT Press
- Carpenter, G. A. and Grossberg, S., 1987, 'ART2: Self-Organization of Stable Category Recognition Codes for Analog Input Patterns,' Applied Optics, Vol.26, No.23, pp. 4919-4930 https://doi.org/10.1364/AO.26.004919
- Carpenter, G. A. and Grossberg, S., 1987, 'A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine,' Computer Vision, Graphics and Image Processing, Vol.37, pp. 54-115 https://doi.org/10.1016/S0734-189X(87)80014-2
- Lankalapalli, K., Chatterjee, S. and Chang, T.C., 1997, 'Feature Recognition Using ART2: A Self-Organizing Neural Network,' Journal of Intelligent Manufacturing, Vol.8, pp. 203-214 https://doi.org/10.1023/A:1018521207901
- ASME, 1989, 'ASME Boiler and Pressure Vessel Code,' Section III, division 1, NB-3200
- Bannantine, J. A., Comer, J.J. and Handrock, J.L., 1990, 'Fundamentals of Metal Fatigue Analysis,' 1st Edition, Prentice Hall, pp. 5-30