Abstract
The paper demonstrates how to remove the undesired temperature effect from Lamb wave data in order to detect structural damage accurately. The method used is based on the cointegration technique and fractal signal processing. The former relies on the analysis of non-stationary behaviour whereas the latter brings the concept of multi-resolution wavelet decomposition of time series. The results show that self-similar pattern of cointegration residuals is broken when damage is present in the monitored structure. This can be used for effective removal of undesired multiple temperature trends in Lamb waves data. Damage-sensitive features are isolated from temperature variations and damage is effectively detected and classified.
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