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

• Published in: Smart materials and structures Vol. 25; no. 1; pp. 15013 - 15031
• Main Authors: Musafere, F, Sadhu, A, Liu, K
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.01.2016
• 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
• ISSN: 0964-1726; 1361-665X
• DOI: 10.1088/0964-1726/25/1/015013

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.