Stress Classification Using Artificial Neural Networks and Fatigue Life Assessment

인공신경망을 이용한 계측응력 분류 및 피로수명 평가

  • 정성욱 (성균관대학교 기계공학부) ;
  • 장윤석 (성균관대학교 기계공학부) ;
  • 최재붕 (성균관대학교 기계공학부) ;
  • 김영진 (성균관대학교 기계공학부)
  • 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


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