Simultaneous seismic input and state estimation with optimal sensor placement for building structures using incomplete acceleration measurements

Taher, Sdiq Anwar; Li, Jian; Fang, Huazhen

doi:10.1016/j.ymssp.2022.110047

Cited by 7 publications

(13 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When direct displacement measurement is unavailable, model-based approaches to estimate state (displacement) have been proposed using acceleration measurements [3,4]. Specifically, online approaches that perform real-time estimation have been paid much attention to [5]. Nevertheless, computational cost, modeling error, measurement noise, incomplete measurements, and system model requirements limit the applicability of the existing approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, studies have also focused on online input and state estimation using Kalman filter (KF)-based approaches in many applications. Examples include the augmented state Kalman filter for systems with direct feedthrough (ASKF-WD) [9] and without direct feedthrough (ASKF) [5], which uses the prior knowledge of input. In particular, the ASKF and ASKF-WD were successfully used to estimate input and state for an earthquake-excited building structure and a laboratory steel beam excited with external loads [5,9].…”

Section: Introductionmentioning

confidence: 99%

“…Examples include the augmented state Kalman filter for systems with direct feedthrough (ASKF-WD) [9] and without direct feedthrough (ASKF) [5], which uses the prior knowledge of input. In particular, the ASKF and ASKF-WD were successfully used to estimate input and state for an earthquake-excited building structure and a laboratory steel beam excited with external loads [5,9]. With regard to methods that make no assumptions about the input, notable examples include the minimum-variance unbiased state estimation (MVUS) [10] and minimum-variance unbiased input and state estimation filter (MVUIS) [5] for systems without direct feedthrough, and minimum-variance unbiased input and state estimation filter for systems with direct feedthrough (MVUIS-WD) [11].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the ASKF and ASKF-WD were successfully used to estimate input and state for an earthquake-excited building structure and a laboratory steel beam excited with external loads [5,9]. With regard to methods that make no assumptions about the input, notable examples include the minimum-variance unbiased state estimation (MVUS) [10] and minimum-variance unbiased input and state estimation filter (MVUIS) [5] for systems without direct feedthrough, and minimum-variance unbiased input and state estimation filter for systems with direct feedthrough (MVUIS-WD) [11]. In structural dynamics applications, MVUIS and MVUIS-WD were also successfully applied for input and state estimation of earthquake-excited building structures [5] and cantilever steel beam under external force [12].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, studies have also paid attention to enhancing the estimation accuracy. An optimal sensor placement algorithm (OSPA) was developed to improve online input and state estimation accuracy by ensuring important system conditions are met [5]. The developed OSPA was integrated with the MVUIS and ASKF, and its effectiveness was validated through numerical, experimental, and real-world building examples.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Time-delayed join seismic input and state estimation for building structures using incomplete acceleration measurements

Taher

2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

View full text Add to dashboard Cite

This paper studies time-delayed simultaneous input and state estimation to enhance estimation accuracy for systems without direct feedthrough, such as earthquake-excited building structures, using absolute floor acceleration measurements. Rank matching, strong observability, and invertibility conditions are crucial for the stability and convergence of input and state estimation. Real-time approaches have achieved successful estimations when those conditions are satisfied. However, a dynamic system model often does not hold those conditions when using acceleration measurements, leading to significant errors in the estimations. To this end, the authors recently developed an optimal sensor placement algorithm to ensure the system model holds the above conditions to achieve accurate real-time estimation. However, accurate estimation in some cases remains challenging because of incomplete measurements, modeling error, and measurement noise. This paper proposes an extended time-delayed joint input and state estimation algorithm (ETDIS) based on the invertibility matrix. Specifically, by incorporating the prior knowledge of the input, the proposed ETDIS is designed from a Bayesian perspective, considering measurement noise to enhance estimation accuracy. In particular, the innovation is used to obtain the input estimation, which is interconnected with the state space equation for state estimation. ETDIS relaxes the rank-matching condition and is more robust against the lack of conditions. Accurate online input and state estimation with a delay and satisfactory computational cost is achieved by utilizing the proposed ETDIS and limited acceleration measurements. Numerical studies are presented to verify the effectiveness of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%