Publication

Hyperautomation Artificial Intelligence

2020 Fatigue Crack Length Estimation and Prediction using Trans-Fitting with Support Vector Regression

본문

Journal
International Journal of Prognostics and Health Management
Author
Myeongbaek Youn, Yunhan Kim, Dongki Lee, Minki Cho, and Byeng D. Youn*
Date
2020-12
Citation Index
IF: 0.0, Rank: 0.0%
Vol./ Page
Vol. 11, No. 1, pp. 1-14
Year
2020

Abstract


A method is described in this paper for crack propagation prediction using only the initial crack length of the target specimen. The proposed method consists of two parts, (1) crack length estimation using support vector regression (SVR) and (2) crack length prediction using a new trans-fitting method. Features based on the filtered wave signals were defined and a model was constructed using the SVR method to estimate the crack length. The hyper-parameters of the SVR model were selected based on a grid search algorithm. Prediction of the crack length was based on the previous crack length, which was estimated based on the wave signals. In this step, a newly proposed trans-fitting method was applied. The proposed trans-fitting method updated the selected candidate function to translocate the trend of crack propagation based on the training dataset. By translocating the trends to the estimated crack length of the target specimen, the crack propagation could be predicted. The proposed method was validated by comparison with given specimens. The results show that the proposed method can estimate and predict the crack length accurately.