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Lamb waves are often used in smart structures with integrated, low-profile piezoceramic transducers for damage detection. However, it is well known that the method is prone to contamination from a variety of interference sources including environmental and operational conditions. The paper demonstrates how to remove the undesired temperature effect from Lamb wave data. The method is based on the concept of cointegration that is partially built on the analysis of the non-stationary behaviour of time series. Instead of directly using Lamb wave responses for damage detection, two approaches are proposed: (i) analysis of cointegrating residuals obtained from the cointegration process of Lamb wave responses, (ii) analysis of stationary characteristics of Lamb wave responses before and after cointegration. The method is tested on undamaged and damaged aluminium plates exposed to temperature variations. The experimental results show that the method can: isolate damage-sensitive features from temperature variations, detect the existence of damage and classify its severity.

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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.

This paper presents a cumulative sum (CUSUM)-based approach for condition monitoring and fault diagnosis of wind turbines (WTs) using SCADA data. The main ideas are to first form a multiple linear regression model using data collected in normal operation state, then monitor the stability of regression coefficients of the model on new observations, and detect a structural change in the form of coefficient instability using CUSUM tests. The method is applied for on-line condition monitoring of a WT using temperature-related SCADA data. A sequence of CUSUM test statistics is used as a damage-sensitive feature in a control chart scheme. If the sequence crosses either upper or lower critical line after some recursive regression iterations, then it indicates the occurrence of a fault in the WT. The method is validated using two case studies with known faults. The results show that the method can effectively monitor the WT and reliably detect abnormal problems.