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Transfer Component Analysis for Compensation of Temperature Effects on the Impedance-Based Structural Health Monitoring by Samuel da Silva; Marcus Omori Yano; Camila Gianini Gonsalez-Bueno is a Engineering article available to read on EtoBox.

What is Transfer Component Analysis for Compensation of Temperature Effects on the Impedance-Based Structural Health Monitoring about?

The effects of temperature fluctuations in the impedance measurements' spectral estimates confuse the procedures to distinguish actual states' classification, demanding compensation. The present paper demonstrates a new method to achieve temperature compensation based on a Transfer Component Analysis (TCA), a subtype of transfer learning, of the features from a source domain (in a well-known labeled condition) to another target domain (in an unknown condition). This procedure assumes only the labeled features data in the healthy condition (baseline) and damaged state in a specific known temperature as source data. The features computed are the Root Mean Square Deviation (RMSD) indices of the real and imaginary impedance signals. A machine-learning algorithm based on Mahalanobis squared distance (D 2 ) is trained using the features computed from the baseline condition in the reference temperature. Also, the other temperature and structural conditions data are assumed as testing data of the target condition. TCA's main idea is mapping the features from the original features space to a new subspace where the detection becomes possible using the same training data in the source domain.

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Author

Samuel da Silva; Marcus Omori Yano; Camila Gianini Gonsalez-Bueno

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Engineering (Physical Sciences)