인공 신경망을 이용한 광대역 과정의 피로 손상 모델 개발 (Development of a Fatigue Damage Model of Wideband Process using an Artificial Neural Network)

서지정보

· 발행기관 : 대한조선학회
· 수록지 정보 : 대한조선학회 논문집 / 52권 / 1호 / 88 ~ 95페이지
· 저자명 : 김호성, 안인규, 김유일

초록

For the frequency-domain spectral fatigue analysis, the probability density function of stress range needs to be estimated based on thestress spectrum only, which is a frequency domain representation of the response. The probability distribution of the stress range of thenarrow-band spectrum is known to follow the Rayleigh distribution, however the PDF of wide-band spectrum is difficult to define withclarity due to the complicated fluctuation pattern of spectrum. In this paper, efforts have been made to figure out the links between theprobability density function of stress range to the structural response of wide-band Gaussian random process. An artificial neuralnetwork scheme, known as one of the most powerful system identification methods, was used to identify the multivariate functionalrelationship between the idealized wide-band spectrums and resulting probability density functions. To achieve this, the spectrums wereidealized as a superposition of two triangles with arbitrary location, height and width, targeting to comprise wide-band spectrum, andthe probability density functions were represented by the linear combination of equally spaced Gaussian basis functions. To train thenetwork under supervision, varieties of different wide-band spectrums were assumed and the converged probability density function ofthe stress range was derived using the rainflow counting method and all these data sets were fed into the three layer perceptronmodel. This nonlinear least square problem was solved using Levenberg-Marquardt algorithm with regularization term included. It wasproven that the network trained using the given data set could reproduce the probability density function of arbitrary wide-bandspectrum of two triangles with great success.

영어초록

For the frequency-domain spectral fatigue analysis, the probability density function of stress range needs to be estimated based on thestress spectrum only, which is a frequency domain representation of the response. The probability distribution of the stress range of thenarrow-band spectrum is known to follow the Rayleigh distribution, however the PDF of wide-band spectrum is difficult to define withclarity due to the complicated fluctuation pattern of spectrum. In this paper, efforts have been made to figure out the links between theprobability density function of stress range to the structural response of wide-band Gaussian random process. An artificial neuralnetwork scheme, known as one of the most powerful system identification methods, was used to identify the multivariate functionalrelationship between the idealized wide-band spectrums and resulting probability density functions. To achieve this, the spectrums wereidealized as a superposition of two triangles with arbitrary location, height and width, targeting to comprise wide-band spectrum, andthe probability density functions were represented by the linear combination of equally spaced Gaussian basis functions. To train thenetwork under supervision, varieties of different wide-band spectrums were assumed and the converged probability density function ofthe stress range was derived using the rainflow counting method and all these data sets were fed into the three layer perceptronmodel. This nonlinear least square problem was solved using Levenberg-Marquardt algorithm with regularization term included. It wasproven that the network trained using the given data set could reproduce the probability density function of arbitrary wide-bandspectrum of two triangles with great success.

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