Abstract
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 (\(\mathcal D^{2}\)) 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. The results performed in a testbench with a piezoelectric element (PZT) bonded under a set of temperatures monitored, and simulated damage confirmed that the proposed method could recognize the real states correctly by transferring the knowledge from the features of the source domain into the target domain, assuming different temperatures.
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Acknowledgements
The authors thank Dr. Fabricio Guimarães Baptista for technical support on the National Instruments DAQ device and LabView software utilized in our experimental tests. The authors also acknowledge the suggestions given by ad hoc reviewers and the Associate Editor.
Funding
The authors thank the financial support provided by Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES/Brasil)-Finance Code 001 and Grant-Number 88882.433643/2019-01, and the Brazilian National Council for Scientific and Technological Development (CNPq) Grant Number 306526/2019-0.
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Silva, S.d., Yano, M.O. & Gonsalez-Bueno, C.G. Transfer Component Analysis for Compensation of Temperature Effects on the Impedance-Based Structural Health Monitoring. J Nondestruct Eval 40, 64 (2021). https://doi.org/10.1007/s10921-021-00794-6
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DOI: https://doi.org/10.1007/s10921-021-00794-6