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Abstract: Data-driven damage-detection schemes are usually unsupervised machine learning models in practice, as these do not require any training. Vibration-based features are commonly used in these schemes but often require several other parameters to accurately correlate with damage, as they may not globally represent the model, making them less sensitive to damage. Modal data, such as frequency Response Functions (FRFs) and principal Component Analysis (PCA) reduced FRFs (PCA-FRFs), inherit the dynamic characteristics of the structure, and it changes when damage occurs, thus showing sensitivity to damage. However, noise from the environment or external sources such as wind, operating machines, or the in-service system itself, can reduce the modal data’s sensitivity to damage if not handled properly, which affects damage-detection accuracy. This paper proposes a noise-robust operational modal-based structural damage-detection scheme that uses impact-Synchronous Modal Analysis (ISMA) to generate clean, static-like FRFs for damage diagnosis. ISMA allows modal data collection without requiring shutdown conditions, and its denoising feature aids in generating clean, static-like FRFs for damage diagnosis. Our results showed that the FRFs obtained through ISMA under noisy conditions have Frequency Response Assurance Criterion (FRAC) and Cross Signature Assurance Criterion (CSAC) scores greater than 0.9 when compared with FRFs obtained through Experimental Modal Analysis (EMA) under static conditions; this validates the denoising feature of ISMA. When the denoised FRFs are reduced to PCA-FRFs and used in an unsupervised Learning-based damage-detection scheme, zero false alarms occur.