Structure Fatigue Crack Length Estimation and Prediction Using Ultrasonic Wave Data Based on Ensemble Linear Regression and Paris’s Law

Meng Rao, Xingkai Yang, Dongdong Wei, Yuejian Chen, Lijun Meng, and Ming J. Zuo
Publication Target: 
IJPHM
Publication Issue: 
2
Submission Type: 
Full Paper
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ijphm_20_011.pdf1.04 MBOctober 26, 2020 - 5:13pm

This paper presents methods for the 2019 PHM Conference Data Challenge developed by the team named "Angler". This Challenge aims to estimate the fatigue crack length of a type of aluminum structure using ultrasonic signals at the current load cycle and to predict the crack length at multiple future load cycles (multiple-step-ahead prediction) as accurately as possible. For estimating crack length, four crack-sensitive features are extracted from ultrasonic signals, namely, the first peak value, root mean square value, logarithm of kurtosis, and correlation coefficient. An ensemble linear regression model is presented to map these features and their second-order interactions with the crack length. The Best Subset Selection method is employed to select the optimal features. For predicting crack length, variations of the Paris’ law are derived to describe the relationships between the crack length and the number of load cycles. The material parameters and stress range of Paris’ law are learned using the Genetic Algorithm. These parameters will be updated based on the previous-step predicted crack length. After that, the crack length corresponding to a future load cycle number for either the constant amplitude load case or variable amplitude load case is predicted. The presented methods achieved a score of 16.14 based on the score-calculation rule provided by the Data Challenge committees, and was ranked third best among all participating teams.

Publication Year: 
2020
Publication Volume: 
11
Publication Control Number: 
011
Page Count: 
14
Submission Keywords: 
Crack propagation
crack prediction
ultrasonic guided waves
Linear Regression
Ensemble
Paris law
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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