Towards damage detection using blind source separation integrated with time-varying auto-regressive modeling

In the 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 structures and mechanical...

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Bibliographic Details
Published in:Smart materials and structures 2016-01, Vol.25 (1), p.15013-15031
Main Authors: Musafere, F, Sadhu, A, Liu, K
Format: Article
Language:English
Subjects:
blind source separation
Damage detection
Mathematical models
Parameter identification
Signal processing
Structural damage
Structural health monitoring
System identification
time-varying auto-regressive model
Vibration
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Summary:In the 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 structures and mechanical systems. 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, and its severity to rehabilitate and prolong the existing health of the structures. In recent years, blind source separation (BSS) algorithm has become one of the newly emerging advanced signal processing techniques for output-only system identification of civil structures. In this paper, a novel damage detection technique is proposed by integrating BSS with the time-varying auto-regressive modeling to identify the instant and severity of damage. The proposed method is validated using a suite of numerical studies and experimental models followed by a full-scale structure.
ISSN:0964-1726
1361-665X
DOI:10.1088/0964-1726/25/1/015013