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Cointegration has been recently brought to structural health monitoring (SHM) as a new methodology for dealing with the problem of environmental and/or operational variability in monitored structures. However, it is well known that the choice of lag length in cointegration analysis has a strong influence on damage detection results. The article presents a new approach for optimal lag length selection in cointegration analysis used for structural damage detection. This new method is based on stationarity analysis of data representing undamaged condition. The proposed method is validated using Lamb wave data under the effects of temperature variations and vibroacoustic data obtained from nonlinear vibroacoustic modulation experiments with different low-frequency vibration (or modal) excitations. The results demonstrate the effectiveness of the method for structural damage detection based on SHM data heavily affected by environmental or operational conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]