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22 March 2021 Temperature compensation for guided waves using convolutional denoising autoencoders
Mahindra Rautela, Senthilnath Jayavelu, Jochen Moll, Srinivasan Gopalakrishnan
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Proceedings Volume 11593, Health Monitoring of Structural and Biological Systems XV; 1159319 (2021) https://doi.org/10.1117/12.2582986
Event: SPIE Smart Structures + Nondestructive Evaluation, 2021, Online Only
Abstract
The effect of temperature on guided waves is considered one of the crucial aspects of a structural health monitoring procedure. The influence of temperature can cause abrupt variations in the actual signatures and interfere with the existing damage identification strategies. In this paper, we have addressed this issue with a self-supervised deep learning-based temperature compensation methodology. We have used temperature affected time-traces from the dataset and converted them into 2D-representation using continuous wavelet transformation. We have proposed a new philosophy of temperature compensation in which the effect of temperature on the reference signals modifies amplitude and phase of the signal, is considered noise. We have trained a convolutional denoising autoencoder to transform temperature affected signals at any temperature into signals at the reference temperature. The performance of the algorithms is evaluated against unseen examples. It is seen that the proposed methodology can successfully compensate for temperature effects with a low mean squared loss and mean squared error and a high coefficient of determination.
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Mahindra Rautela, Senthilnath Jayavelu, Jochen Moll, and Srinivasan Gopalakrishnan "Temperature compensation for guided waves using convolutional denoising autoencoders", Proc. SPIE 11593, Health Monitoring of Structural and Biological Systems XV, 1159319 (22 March 2021); https://doi.org/10.1117/12.2582986
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KEYWORDS
Waveguides
Data modeling
Structural health monitoring
Temperature metrology
Time-frequency analysis
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