Discrete-Time ARMAv Model-Based Optimal Sensor Placement
- Washington University in St. Louis, St. Louis, MO 63130 (United States)
This paper concentrates on the optimal sensor placement problem in ambient vibration based structural health monitoring. More specifically, the paper examines the covariance of estimated parameters during system identification using auto-regressive and moving average vector (ARMAv) model. By utilizing the discrete-time steady state Kalman filter, this paper realizes the structure's finite element (FE) model under broad-band white noise excitations using an ARMAv model. Based on the asymptotic distribution of the parameter estimates of the ARMAv model, both a theoretical closed form and a numerical estimate form of the covariance of the estimates are obtained. Introducing the information entropy (differential entropy) measure, as well as various matrix norms, this paper attempts to find a reasonable measure to the uncertainties embedded in the ARMAv model estimates. Thus, it is possible to select the optimal sensor placement that would lead to the smallest uncertainties during the ARMAv identification process. Two numerical examples are provided to demonstrate the methodology and compare the sensor placement results upon various measures.
- OSTI ID:
- 21148899
- 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: 10.1063/1.2963751; (c) 2008 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
- Country of Publication:
- United States
- Language:
- English
Similar Records
Fuzzy modeling of analytical redundancy for sensor failure detection
Sensor placement algorithm development to maximize the efficiency of acid gas removal unit for integrated gasifiction combined sycle (IGCC) power plant with CO2 capture