1.1.SHM and similar research fields 1.2.Effects of varying environmental and operational conditions on SHM 1.3.Why cointegration has been applied to SHM 1.4.A review of cointegration-based approaches for SHM 1.5.Motivation and scope of this monograph 2.Stationarity and nonstationarity 2.1.Definitions and basic concepts 2.2.Time series and stationarity 2.3.Unit root tests 2.4.The Dickey-Fuller (DF) and augmented Dickey-Fuller (ADF) tests 3.Cointegration method 3.1.Introduction to cointegration 3.2.Cointegration and common trends 3.3.Testing for cointegration 3.4.Johansen's cointegration procedure 3.5.Testing for stationarity 3.6.Example using the Weierstrass-Mandelbrot cosine fractal function 3.7.Summary and discussion 4.Lag length selection in cointegration analysis used for SHM 4.1.Background 4.2.Conventional selection methods from econometrics 4.2.1.Methods based on the information criteria 4.2.2.Methods based on the likelihood ratio test 4.2.3.Methods based on the sequential modified likelihood ratio test 4.2.4.Sample size and lag length selection 4.3.Optimal lag length selection based on stationarity analysis used for structural damage detection 4.4.Summary and conclusions 5.Cointegration-based approach to SHM applications 5.1.Damage detection scenarios 5.1.1.Using geometrical features of cointegration residuals 5.1.2.Using wavelet variance characteristics of cointegration residuals 5.1.2.1.Fractal-based signal processing using wavelets 5.1.2.2.Wavelet-based fractal analysis of cointegration residuals 5.1.3.Using stationary statistical characteristics of cointegration residuals 5.2.Case study 1: Structural damage detection in aluminium plates using lamb waves under temperature variations 5.2.1.Lamb wave data contaminated by temperature 5.2.2.Lag length selection results 5.2.3.Damage detection results using cointegration residuals 5.2.4.Damage detection results using wavelet variance characteristics of cointegration residuals 5.2.5.Damage detection results using stationary statistical characteristics of cointegration residuals 5.3.Case study 2: Impact damage detection in composite plates using nonlinear acoustics under load changes 5.3.1.Principle of nonlinear vibro-acoustic wave modulation technique 5.3.2.Vibro-acoustic data for different frequencies of modal excitations 5.3.3.Lag length selection results 5.3.4.Damage detection results using stationary statistical characteristics of cointegration residuals 5.4.Summary and conclusions 6.Cointegration-based approach to condition monitoring of wind turbines 6.1.Condition monitoring and fault diagnosis of wind turbines using SCADA data 6.2.1.Review of previous work 6.2.2.Discussion 6.3.Cointegration-based approach to condition monitoring of wind turbines 6.4.Experimental wind turbine data 6.5.Case study 1: Using various process parameters of the wind turbine 6.5.1.Optimal cointegrating vectors 6.5.2.Condition monitoring and fault detection using cointegration residuals 6.5.3.Discussion 6.6.Case study 2: Using only the temperature data of gearbox and generator