Data normalisation for Lamb wave–based damage detection using cointegration: A case study with single- and multiple-temperature trends

Abstract

This article presents an application of the cointegration technique for temperature effect removal (i.e. data normalisation) in Lamb wave–based damage detection. The method is based on the concept of stationarity of time series. Analysis of cointegrating residuals and stationary statistical characteristics – before and after the cointegration process – are used for damage detection. The method is validated using Lamb wave data from undamaged and damaged aluminium plates instrumented with low-profile, surface-bonded piezoceramic transducers. Two temperature variation cases (single- and multiple-temperature trends) are investigated. The experimental results show that the cointegration process can remove undesired temperature effects and accurately detect damage.