Abstract
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.
Highlights
Fatigue cracks are a common type of faults in structural systems
In this study, an optimization framework based on the Genetic Algorithm (GA) is proposed to obtain the optimal material parameters and equivalent effect of the variable amplitude load, which enables the Paris’ Law model to predict crack lengths under variable load scenarios
To overcome the challenges of limited data amount and variable load conditions for structure crack length estimation and prediction, models based on physical knowledge and data-driven methods were proposed
Summary
Fatigue cracks are a common type of faults in structural systems. They contribute to about 90% of the failures of metallic structures (Campbell, 2018). INTERNATIONAL JOURNAL OF PROGNOSTICS AND HEALTH MANAGEMENT energy ratio change from ultrasonic signals This feature was fed into an artificial neural network (ANN) to diagnose the crack length and location of plates. Simpler data-driven models often perform better when the data amount is limited He et al (2013) built a linear regression model based on three features extracted from ultrasonic signals to quantify the crack length of riveted lap joints. Using the Paris’ Law model and its variations can predict crack growth under constant or variable amplitude load if the material parameters are known. In this study, an optimization framework based on the Genetic Algorithm (GA) is proposed to obtain the optimal material parameters and equivalent effect of the variable amplitude load, which enables the Paris’ Law model to predict crack lengths under variable load scenarios.
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