Random-vibration-based statistical time series structural health monitoring methods utilize small-scale, compact, and data-based, time series stochastic representations of the structural dynamics for damage diagnosis. In this study, a comprehensive and critical assessment of the diagnostic performance of five prominent response-only methods is presented based on incipient, ‘minor’ to ‘mild’, damages on a lab-scale wind turbine jacket structure. Statistically reliable damage detection and identification results are obtained via a ‘rotation’ procedure resulting into thousands of test cases, with the performance analysed in terms of receiver operating characteristic curves and confusion matrices. The results indicate not only challenging of the methods’ capabilities, but also the achievement of good to excellent performance for the ‘minor’ to ‘mild’ damages, respectively, with the model parameter–based method offering the best performance. In addition, the use of a vibration signal measured via a laser vibrometer leads to slightly improved detection performance over that obtained via a classical accelerometer.
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