A consistent-mode indicator for the eigensystem realization algorithm

Pappa, Richard S.; Elliott, Kenny B.; Schenk, Axel

doi:10.2514/6.1992-2136

Cited by 49 publications

(35 citation statements)

References 4 publications

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“…To minimize estimation error, especially when the signal‐to‐noise ratio is low, the ARX model order is usually set higher than the actual model; therefore, the fitted model may contain more poles and zeros in the transfer function than the actual structure with a certain sampling frequency. To distinguish effectively the structural modes with the noise modes, two noise mode indicators associated with the ERA, namely, the MPC and the EMAC are used (see Juang and Pappa and Pappa and Elliott for more details). Herein, the impulse response function extracted from the fitted ARX model is used as input to the ERA; subsequently, the identified modes with EMAC and MPC values higher than the predetermined thresholds are considered as candidates of structural modes.…”

Section: Arx‐era Methods For System Identificationmentioning

confidence: 99%

Parametric time‐domain identification of multiple‐input systems using decoupled output signals

Ruiz-Sandoval

Spencer

et al. 2013

Earthq Engng Struct Dyn

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SUMMARYCivil engineering structures are often subjected to multidirectional actions such as earthquake ground motion, which lead to complex structural responses. The contributions from the latter multidirectional actions to the response are highly coupled, leading to a MIMO system identification problem. Compared with single‐input, multiple‐output (SIMO) system identification, MIMO problems are more computationally complex and error prone. In this paper, a new system identification strategy is proposed for civil engineering structures with multiple inputs that induce strong coupling in the response. The proposed solution comprises converting the MIMO problem into separate SIMO problems, decoupling the outputs by extracting the contribution from the respective input signals to the outputs. To this end, a QR factorization‐based decoupling method is employed, and its performance is examined. Three factors, which affect the accuracy of the decoupling result, including memory length, input correlation, and system damping, are investigated. Additionally, a system identification method that combines the autoregressive model with exogenous input (ARX) and the Eigensystem Realization Algorithm (ERA) is proposed. The associated extended modal amplitude coherence and modal phase collinearity are used to delineate the structural and noise modes in the fitted ARX model. The efficacy of the ARX‐ERA method is then demonstrated through identification of the modal properties of a highway overcrossing bridge. Copyright © 2013 John Wiley & Sons, Ltd.

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Section: Arx‐era Methods For System Identificationmentioning

confidence: 99%

Parametric time‐domain identification of multiple‐input systems using decoupled output signals

Ruiz-Sandoval

Spencer

et al. 2013

Earthq Engng Struct Dyn

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“…From Figure , an ARX model order of 42 was found necessary to achieve stable modes. Another index, modal phase collinearity (MPC) , was used to distinguish actual modes from spurious ones that were an artifact of the computation. The MPC index for an actual structural mode is close to unity, and a cutoff MPC of 0.90 was selected for this study.…”

Section: System Identification and Dynamic Properties Of The Specimenmentioning

confidence: 99%

Damping identification of a full‐scale passively controlled five‐story steel building structure

Hikino

Kasai

et al. 2012

Earthq Engng Struct Dyn

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SUMMARY A series of large‐scale dynamic tests was conducted on a passively controlled five‐story steel building on the E‐Defense shaking table facility in Japan to accumulate knowledge of realistic seismic behavior of passively controlled structures. The specimen was tested by repeatedly inserting and replacing each of four damper types, that is, the buckling restrained braces, viscous dampers, oil dampers, and viscoelastic dampers. Finally, the bare steel moment frame was tested after removing all dampers. A variety of excitations was applied to the specimen, including white noise, various levels of seismic motion, and shaker excitation. System identification was implemented to extract dynamic properties of the specimen from the recorded floor acceleration data. Damping characteristics of the specimen were identified. In addition, simplified estimations of the supplemental damping ratios provided by added dampers were presented to provide insight into understanding the damping characteristics of the specimen. It is shown that damping ratios for the specimen equipped with velocity‐dependent dampers decreased obviously with the increasing order of modes, exhibiting frequency dependency. Damping ratios for the specimen equipped with oil and viscoelastic dampers remained constant regardless of vibration amplitudes, whereas those for the specimen equipped with viscous dampers increased obviously with an increase in vibration amplitudes because of the viscosity nonlinearity of the dampers. In very small‐amplitude vibrations, viscous and oil dampers provided much lower supplemental damping than the standard, whereas viscoelastic dampers could be very efficient. Copyright © 2012 John Wiley & Sons, Ltd.

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“…Moreover, to compare the accuracy of the identified modes, consistent mode indicators (CMI), developed by Pappa et al . are calculated (for both methods) and presented in this table. To calculate these indicators along with the modal parameters of the system, ERA is used.…”

Section: Iterative Modal Identification For Output‐only Identificationmentioning

confidence: 99%

“…To calculate these indicators along with the modal parameters of the system, ERA is used. CMI is defined as the product of modal amplitude coherence (MAC) and modal phase collinearity (MPC), both of which can be extracted when ERA is used. MAC measures the coherence between extracted modal amplitude history and the modal amplitude which is formed by extrapolating the initial value to later points in time, using the identified eigenvalues.…”

Section: Iterative Modal Identification For Output‐only Identificationmentioning

confidence: 99%

Stochastic iterative modal identification algorithm and application in wireless sensor networks

Dorvash

Pakzad

2012

Struct. Control Health Monit.

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SUMMARY Computational capability of wireless sensor network (WSN) significantly facilitates application of dense sensory arrays, which is increasingly important in health monitoring of large‐scale structural systems. As the wireless sensor technology improves, more complicated tasks can be assigned to sensing units, and the communication between sensing nodes and their base station can be minimized, utilizing in‐network processing. This strategy should be used to address WSN challenges, such as limited communication bandwidth and prohibitive power consumption, associated with wireless communication and battery power. An iterative modal identification algorithm is proposed in this paper, which uses the on‐board processors for estimation of system parameters through iteration cycles. The iterative algorithm was originally developed such that each individual sensor, having an initial estimate of the system parameters, its local measurement, and the excitation signal, updates the estimated model and passes it through the network until convergence. This study further improves the algorithm to eliminate its limitations in need for availability of excitation load and initial estimate of the system parameters. As a result, the algorithm is applicable for modal identification of structural systems under ambient loading without need for prior information about the system parameters. The development of the algorithm is presented in this paper and validated through implementation on a numerically simulated example and a laboratory experiment. Furthermore, its performance is evaluated using data from an ambient vibration test of the Golden Gate Bridge using a WSN. Results of these implementations verify the functionality of the algorithm in monitoring of real‐life structural systems. Copyright © 2012 John Wiley & Sons, Ltd.

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A consistent-mode indicator for the eigensystem realization algorithm

Cited by 49 publications

References 4 publications

Parametric time‐domain identification of multiple‐input systems using decoupled output signals

Parametric time‐domain identification of multiple‐input systems using decoupled output signals

Damping identification of a full‐scale passively controlled five‐story steel building structure

Stochastic iterative modal identification algorithm and application in wireless sensor networks

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