| dc.description.abstract | 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. | en_US |
