Real-Time Modal Parameter Estimation Using Subspace Methods: Applications

Bosse, Albert; Tasker, Frederick; Fisher, Shalom

doi:10.1006/mssp.1998.0162

Cited by 23 publications

(14 citation statements)

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“…Examples are a rocket burning fuel or dangerous and expensive flight flutter tests, requiring fast-online data processing techniques for which some possible approaches are discussed in [13][14][15]. On the other hand, numerous other vibration-based condition monitoring applications are conducted on a totally different time scale, resulting in sufficient time for on-line data analysis using more accurate algorithms such as the MLE.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Structural Health Monitoring—part I: Modal Parameter Estimation and Tracking

Verboven

Parloo

Guillaume

et al. 2002

Mechanical Systems and Signal Processing

122

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Section: Introductionmentioning

confidence: 99%

Autonomous Structural Health Monitoring—part I: Modal Parameter Estimation and Tracking

Verboven

Parloo

Guillaume

et al. 2002

Mechanical Systems and Signal Processing

122

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“…Research efforts in this area have resulted in the development of many time-domain methods. A number of these methods involve recursive algorithms based on subspace [1][2][3] or autoregressive and moving average (ARMA) model [4]. Some others track time-varying systems by applying moving windows on time histories.…”

Section: Introductionmentioning

confidence: 99%

Non-stationary frequency domain system identification using time–frequency representations

Guo

Kareem

2016

Mechanical Systems and Signal Processing

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System properties of buildings and bridges may vary with time due to temperature changes, aging or extreme loadings. To identify these time-varying system properties, this study proposes a new output-only non-stationary system identification (SI) framework based on instantaneous or marginal spectra derived from the time-frequency representation, e.g., short time Fourier or wavelet transform. Spectra derived from these time-frequency representations are very popular in tracking time-varying frequencies; however, they have seldom been used to identify the time-varying damping ratio because a short window needed to capture the timevarying information amplifies the bandwidth significantly, which may lead to considerably overestimating the damping ratio. To overcome this shortcoming, this study modifies the theoretical frequency response function (FRF) to explicitly account for the windowing effect, and therefore enables SI directly using instantaneous or marginal spectra derived from the wavelet or short time Fourier transform. The response spectrum estimated using the short time window and the modified FRF are both influenced by the same time window, thus the instantaneous or time-localized marginal spectrum of response can be fitted to the modified FRF to identify frequency and damping ratio at each time instant. This spectral-based SI framework can reliably identify damping in time-varying systems under non-stationary excitations. The efficacy of the proposed framework is demonstrated by both numerical and full-scale examples, and also compared to the time-domain SI method, e.g. stochastic subspace identification (SSI),

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“…Various versions of subspace method have been used to identify the state space model of linear systems [13,14], which include the well-known algorithms CVA, N4SID, MOESP, and IV-4SID [12,[14][15][16]]. An overview and application of SI to modal parameter estimation and damage detection of structure had been studied [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Application of subspace identification technique to long‐term seismic response monitoring of structures

Loh

Chao

Weng³

et al. 2014

Earthq Engng Struct Dyn

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This paper presents the identification and assessment of the dynamic characteristics of building and bridge structures from the long-term monitoring of seismic response data. Over 40 events of the seismic response data of these structures during the past 10 years were used. To extract the dynamic characteristics of the structures from its seismic response data, the time-domain data-driven subspace identification (SI-DATA) technique is used to extract the system natural frequencies, damping ratios and mode shapes. To enhance the stability of the identified real system poles in using SI-DATA, an algorithm of removing spurious modes is used. Discussion on the identified system dynamic characteristics of the structures under different intensity level of ground excitation is presented. Effect of soil-structural interaction on the identified system natural frequencies and damping ratios by considering either free-field data or basement data as input in subspace identification is also discussed.

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Real-Time Modal Parameter Estimation Using Subspace Methods: Applications

Cited by 23 publications

References 0 publications

Autonomous Structural Health Monitoring—part I: Modal Parameter Estimation and Tracking

Autonomous Structural Health Monitoring—part I: Modal Parameter Estimation and Tracking

Non-stationary frequency domain system identification using time–frequency representations

Application of subspace identification technique to long‐term seismic response monitoring of structures

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