A System Identification and Change Detection Methodology for Stochastic Nonlinear Dynamic Systems

Journal Article · Tue Jul 08 00:00:00 EDT 2008 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.2963744· OSTI ID:21148892

Yun, Hae-Bum; Masri, Sami F; Caffrey, John P ^[1]

---

1. University of Southern California, Los Angeles, CA (United States)

In this paper a component-level detection methodology for system identification and change detection is discussed. The methodology is based on non-parametric, data-driven, stochastic system identification classifications using statistical pattern recognition techniques. In order to validate the methodology discussed in this paper an experimental study was performed using a complex nonlinear magneto-rheological (MR) damper. The results of this study show that the proposed methodology is very promising to detect interpret changes in critical structural components such as nonlinear springs joints as well as various types of dampers.

• OSTI ID: 21148892
• Journal Information: AIP Conference Proceedings, Vol. 1020, Issue 1; Conference: 2008 seismic engineering conference: Commemorating the 1908 Messina and Reggio Calabria earthquake, Reggio Calabria (Italy), 8-11 Jul 2008; Other Information: DOI: 10.1063/1.2963744; (c) 2008 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES
JOINTS
NONLINEAR PROBLEMS
NUMERICAL ANALYSIS
PATTERN RECOGNITION
SEISMIC DETECTION
SEISMOLOGY
SPRINGS
STOCHASTIC PROCESSES