Temperature compensation for guided waves using convolutional denoising autoencoders
Mahindra Rautela, J. Senthilnath, Jochen Moll, S. Gopalakrishnan
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.