Efficient sensor placement for state estimation in structural dynamics. (15th February 2017)

• Record Type: Journal Article
• Authors: Hernandez, Eric M.
• Is Part Of: Mechanical systems and signal processing. Volume 85(2017)
• Page Start: 789
• Page End: 800
• Publication Date: 2017-02-15
• Subjects: Kalman filter -- Sensor placement -- State estimation -- Stress estimation -- Structural dynamics; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.09.005
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: This paper derives a computationally efficient algorithm to determine optimal sequential sensor placement for state estimation in linear structural systems subjected to unmeasured excitations and noise contaminated measurements. The proposed algorithm is developed within the context of the Kalman filter and it minimizes the variance of the state estimate among all possible sequential sensor locations. The paper investigates the effects of measurement type, covariance matrix partition selection, spatial correlation of excitation and model selection on optimal sensor placement. The paper shows that the sequential approach reaches the optimal sensor placement as the number of sensor increases. Highlights: A sequential algorithm to determine optimal sensor placement for state estimation is presented. The proposed algorithm is orders of magnitude more efficient that exhaustive search. The effects of measurement type, input correlation and model selection are considered. The proposed algorithm was found to be more effective than the observability criterion.