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

Myeongbaek Youn, Yunhan Kim, Dongki Lee, Minki Cho, and Byeng D. Youn
Publication Target: 
IJPHM
Publication Issue: 
1
Submission Type: 
Full Paper
Supporting Agencies (optional): 
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No. 2020R1A2C3003644)
AttachmentSizeTimestamp
ijphm_20_006.pdf1.04 MBAugust 18, 2020 - 11:05pm

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.

Publication Year: 
2020
Publication Volume: 
11
Publication Control Number: 
006
Page Count: 
14
Submission Keywords: 
fatigue crack growth
Estimation
prediction
Data Challenge
support vector regression
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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