A supervised outlier analysis for risk assessment in composite wing structures

Yingtao Liu; Bach Duong

doi:10.1117/12.2086524

1 April 2015 A supervised outlier analysis for risk assessment in composite wing structures

Yingtao Liu, Bach Duong

Proceedings Volume 9437, Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure 2015; 94370E (2015) https://doi.org/10.1117/12.2086524
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2015, San Diego, California, United States

Abstract

Impact damage has been identified as a critical form of defect that constantly threatens the reliability of composite structures, such as those used in aircrafts and naval vessels. Low energy impacts can introduce barely visible damage and cause structural degradation. Therefore, efficient damage detection and risk assessment methods, which can accurately detect, quantify, and localize impact damage in complex composite structures, are required. In this paper a novel damage detection methodology is demonstrated for monitoring and quantifying the impact damage propagation. Statistical outlier analysis, composed of features extracted from the time and frequency domains, are developed. Autoregression with exogenous is used to classify the statistical feature and estimate the structural risk. The developed methodology has been validated using low velocity impact experiments with a sandwich composite wing.

Citation Download Citation

Yingtao Liu and Bach Duong "A supervised outlier analysis for risk assessment in composite wing structures", Proc. SPIE 9437, Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure 2015, 94370E (1 April 2015); https://doi.org/10.1117/12.2086524

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