in last few decades, structural health monitoring (shm) has been an indispensable subject in the field of vibration engineering. with the aid of modern sensing technology, shm has garnered significant attention towards diagnosis and risk management of large-scale civil and mechanical structures. in shm, system identification is one of major building blocks through which unknown system parameters are extracted from vibration data of the structures. such system information is then utilized to detect the damage instant, severity and extent to rehabilitate and prolong the existing health of the structures. in recent years, blind source separation (bss) has become one of the newly emerging advanced signal processing techniques for output-only system identification of structures. this is attractive for large structures since the input information is not readily available. in this work, two new damage detection techniques are proposed integrating a special class of bss known as second-order blind identification (sobi); first with the hilbert transform (ht) and second with the time-varying auto-regressive (tvar) modeling to track the change of modal parameters of the structure. the proposed method is validated considering discrete damage cases in a suite of numerical studies and experimental models followed by a full-scale structure. the results are then compared with finite element (fe) modeling in case of lab-scale study and with the stochastic subspace identification (ssi) method in the case of full-scale structure. the proposed method (sobi with tvar) is then employed to identify the instantaneous frequencies (if) of an axially-moving cantilever beam simulating the case of progressive damage. identification of the ifs is also carried out using three different algorithms namely the wavelet transform (wt), the hilbert vibration decomposition (hvd), and the hvd plus the tvar modeling.