scholarly journals Response-Only Parametric Estimation of Structural Systems Using a Modified Stochastic Subspace Identification Technique

2021 ◽  
Vol 11 (24) ◽  
pp. 11751
Author(s):  
Chang-Sheng Lin ◽  
Yi-Xiu Wu
Keyword(s):  
Time Moment ◽  
Parametric Identification ◽  
Parametric Estimation ◽  
Data Matrix ◽  
Subspace Identification ◽  
System Matrix ◽  
State Matrix ◽  
Stochastic Subspace Identification ◽  
Identification Technique ◽  
Output Only

The present paper is a study of output-only modal estimation based on the stochastic subspace identification technique (SSI) to avoid the restrictions of well-controlled laboratory conditions when performing experimental modal analysis and aims to develop the appropriate algorithms for ambient modal estimation. The conventional SSI technique, including two types of covariance-driven and data-driven algorithms, is employed for parametric identification of a system subjected to stationary white excitation. By introducing the procedure of solving the system matrix in SSI-COV in conjunction with SSI-DATA, the SSI technique can be efficiently performed without using the original large-dimension data matrix, through the singular value decomposition of the improved projection matrix. In addition, the computational efficiency of the SSI technique is also improved by extracting two predictive-state matrixes with recursive relationship from the same original predictive-state matrix, and then omitting the step of reevaluating the predictive-state matrix at the next-time moment. Numerical simulations and experimental verification illustrate and confirm that the present method can accurately implement modal estimation from stationary response data only.

Stochastic System Identification for Operational Modal Analysis: A Review

2001 ◽  
Vol 123 (4) ◽  
pp. 659-667 ◽  
Author(s):  
Bart Peeters ◽  
Guido De Roeck
Keyword(s):  
System Identification ◽  
Stochastic System ◽  
Instrumental Variable ◽  
Subspace Identification ◽  
Response Functions ◽  
Impulse Responses ◽  
Identification Methods ◽  
Operational Conditions ◽  
Stochastic Subspace Identification ◽  
Output Only

This paper reviews stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions. It is found that many classical input-output methods have an output-only counterpart. For instance, the Complex Mode Indication Function (CMIF) can be applied both to Frequency Response Functions and output power and cross spectra. The Polyreference Time Domain (PTD) method applied to impulse responses is similar to the Instrumental Variable (IV) method applied to output covariances. The Eigensystem Realization Algorithm (ERA) is equivalent to stochastic subspace identification.

Extraction of modal parameters for identification of time-varying systems using data-driven stochastic subspace identification

2017 ◽  
Vol 24 (20) ◽  
pp. 4781-4796 ◽  
Author(s):  
Wenchao Li ◽  
Viet-Hung Vu ◽  
Zhaoheng Liu ◽  
Marc Thomas ◽  
Bruce Hazel
Keyword(s):  
Data Driven ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Time Varying ◽  
Lumped Mass ◽  
State Matrix ◽  
Mass System ◽  
Stochastic Subspace Identification ◽  
Time Varying Systems ◽  
Using Data

This paper presents a method for the extraction of modal parameters for identification of time-varying systems using Data-Driven Stochastic Subspace Identification (SSI-DATA). In practical applications of SSI-DATA, both the modal parameters and computational ones are mixed together in the identified results. In order to differentiate the structural ones from computational ones, a new method based on the eigen-decomposition of the state matrix constructed in SSI-DATA is proposed. The efficiency of the proposed method is demonstrated through numerical simulation of a lumped-mass system and experimental test of a moving robot for extracting excited natural frequencies of the system.

scholarly journals OUTPUT-ONLY MODAL IDENTIFICATION OF TENSEGRITY STRUCTURES

2016 ◽  
Vol 8 (2) ◽  
pp. 52-64 ◽  
Author(s):  
Miniar Attig ◽  
Maher Abdelghani ◽  
Nabil ben Kahla
Keyword(s):  
Natural Frequencies ◽  
Modal Identification ◽  
Mode Shapes ◽  
Subspace Identification ◽  
Balanced Realization ◽  
Stochastic Subspace Identification ◽  
Tensegrity Systems ◽  
Stabilization Diagram ◽  
Subspace Algorithms ◽  
Output Only

Tensegrity systems are a special class of spatial reticulated structures that are composed of struts in compression and cables in tension. In this paper, the performance of stochastic subspace algorithms for modal identification of complex tensegrity systems is investigated. A sub-class algorithm of the Stochastic Subspace Identification family: the Balanced Realization Algorithm is investigated for modal identification of a tripod simplex structure and a Geiger dome. The presented algorithm is combined with a stabilization diagram with combined criteria (frequency, damping and mode shapes). It is shown that although the studied structures present closely spaced modes, the Balanced Realization Algorithm performs well and guarantees separation between closely-spaced natural frequencies. Modal identification results are validated through comparisons of the correlations (empirical vs. model based) showing effectiveness of the proposed methodology.

Experimental Modal Analysis of Jacket-Type Platforms Using Data-Driven Stochastic Subspace Identification Method

2012 ◽  
Author(s):  
Junfeng Xin ◽  
Sau-Lon James Hu ◽  
Huajun Li
Keyword(s):  
White Noise ◽  
Impact Loading ◽  
Modal Identification ◽  
Data Driven ◽  
Subspace Identification ◽  
Stochastic Subspace Identification ◽  
Gaussian White Noise Process ◽  
Using Data ◽  
Output Only ◽  
The Impact

Employing efficient techniques to accurately identify the modal parameters of new and aging offshore structures has been of interest to the offshore industry for decades. Early methods of modal identification were developed for the frequency domain. The new trend is to employ either input-output or output-only time-domain modal identification methods. Under the assumption that the excitation input is a zero-mean Gaussian white noise process, a modern output-only method that allows direct application to the response time series is the data-driven stochastic subspace identification (SSI-data) method. The main objective of this paper is to evaluate the performance of the SSI-data method using the test data measured from a physical model of a realistic offshore jacket-type platform. Response acceleration data associated with three different excitation mechanisms are investigated: impact loading, step relaxation and white noise ground motion. Although the SSI-data method has been theoretically developed, and often perceived to be only valid, for the ambient noise testing environment, it is shown in this study that the SSI-data method also performs well using data from either the impact loading or step relaxation tests.

Damage Localization of Output-Only Frame Systems Using Stochastic Subspace Identification

2012 ◽  
Vol 446-449 ◽  
pp. 1352-1359 ◽  
Author(s):  
Yen Po Wang ◽  
Yi Ting Lin ◽  
Gang Huang
Keyword(s):  
Dynamic Characteristics ◽  
Real Life ◽  
Subspace Identification ◽  
Main Task ◽  
Damage Localization ◽  
Damage State ◽  
System Parameters ◽  
Stochastic Subspace Identification ◽  
Covariance Functions ◽  
Output Only

Assurance of integrity of structures is the main task of structural health monitoring. The condition (health) of a structure may be revealed from its dynamic characteristics in response to natural or man-made loads. As the measurement of the natural or operating forces on actual real-life structures is generally formidable, dynamic characteristics of the structures have to be extracted from the available output signals only. The stochastic subspace identification (SSI) technique is adopted in this study to identify the equivalent system parameters of the discrete-time state equation using covariance functions of the measured output signals. With the system parameters realized, the method of damage locating vector (DLV) is then considered for further assessment. Members with nearly zero stress under the loadings of DLVs are considered potentially damaged, whereas the DLVs are derived from singular value decomposition of the change in flexibility matrix of the structure before and after the damage state. In this study, the feasibility of DLV method for damage detection of a planar structure is explored based on its seismic responses. To comply with the desired output-only scenario, the information of ground motion is discarded in the stage of SSI analysis. Despite the non-stationary nature of earthquakes, the proposed scheme has been proved sufficient for damage localization of structures from global responses (floor accelerations). The damaged locations can be identified when the structure is fully observed, regardless of single or multiple damages. In the case of partial but co-located observation (damaged floors observed), the damaged locations can still be identified with acceptable accuracy and reliability.

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