An Introduction of a Robust OMA Method: CoS‐SSI and Its Performance Evaluation through the Simulation and a Case Study

Liu, Fulong; Wu, Jigang; Gu, Fengshou; Ball, Andrew

doi:10.1155/2019/6581516

Cited by 14 publications

(10 citation statements)

References 18 publications

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“…This is likely an effect of the higher selfnoise of the Zland geophones compared to the broadband TC 20 s seismometer, which may be greater than the excitation level of the higher modes (e.g., Brincker and Larsen, 2007). We were also not able to find a set of SSI-COV parameters that could reliably reproduce the resonant frequencies, which is again attributed to the lower signal-to-noise ratio of the geophone data (e.g., Brincker, 2014;Liu et al, 2019).…”

Section: Differences Between Surveys At Squint Archmentioning

confidence: 85%

An update on techniques to assess normal-mode behavior of rock arches by ambient vibrations

et al. 2021

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Abstract. Natural rock arches are rare and beautiful geologic landforms with important cultural value. As such, their management requires periodic assessment of structural integrity to understand environmental and anthropogenic influences on arch stability. Measurements of passive seismic vibrations represent a rapid and non-invasive technique to describe the dynamic properties of natural arches, including resonant frequencies, modal damping ratios, and mode shapes, which can be monitored over time for structural health assessment. However, commonly applied spectral analysis tools are often limited in their ability to resolve characteristics of closely spaced or complex higher-order modes. Therefore, we investigate two techniques well-established in the field of civil engineering through application to a set of natural arches previously characterized using polarization analysis and spectral peak-picking techniques. Results from enhanced frequency domain decomposition and parametric covariance-driven stochastic subspace identification modal analyses showed generally good agreement with spectral peak-picking and frequency-dependent polarization analyses. However, we show that these advanced techniques offer the capability to resolve closely spaced modes including their corresponding modal damping ratios. In addition, due to preservation of phase information, enhanced frequency domain decomposition allows for direct and convenient three-dimensional visualization of mode shapes. These techniques provide detailed characterization of dynamic parameters, which can be monitored to detect structural changes indicating damage and failure, and in addition have the potential to improve numerical models used for arch stability assessment. Results of our study encourage broad adoption and application of these advanced modal analysis techniques for dynamic analysis of a wide range of geological features.

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Section: Differences Between Surveys At Squint Archmentioning

confidence: 85%

An update on techniques to assess normal-mode behavior of rock arches by ambient vibrations

et al. 2021

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“…The excitation force is simulated as stationary white noise, which is approximately generated as a zeromean band-pass noise [21], whose frequency range is from 0 to 50 Hz, and the standard deviation, i.e., power spectrum density, is 0.04 N 2 •s/rad. The sampling interval is chosen as ∆t = 0.01 s, and the sampling period, as shown in Figure 2, is T = N t •∆t = 1310.72 s, where N t was chosen as 2 17 . The cut-off frequency, ω c is 314.15 rad/s, and the resolution in frequency domain ∆ω is 4.79 × 10 −3 rad/s.…”

Section: Six Dof Chain Model Of a Cantilever Beammentioning

confidence: 99%

“…0.570 0.000574 0.0003616 2 13 2.217 0.000760 0.0005376 2 14 9.074 0.001219 0.0009967 2 15 37.197 0.003244 0.0029074 2 16 Out of memory 0.003264 0.0030135 2 17 Out of memory 0.006189 0.0059404…”

Section: Sampling Pointsmentioning

confidence: 99%

“…In recent years, Stochastic Subspace Identification (SSI), applied with NExT, has been widely employed to modal estimation of structures under ambient vibration [17]. The SSI method is a time-domain modal-estimation method under the assumption of stationary white noise for ambient excitation and can be directly applied to modal estimation from ambient response records only [18].…”

Section: Introductionmentioning

confidence: 99%

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

Lin

2021

Applied Sciences

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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.

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“…For example, the vehicle–bridge vibration structure is a variable mass time-varying structure. 3 In addition, in real life, data are obtained one after another. Therefore, the data processing method should be online and in real time and can adapt to time-varying or non-stationary characteristics.…”

Section: Introductionmentioning

confidence: 99%

An online and real-time adaptive operational modal parameter identification method based on fog computing in Internet of Things

Wang

Huang

Chen

et al. 2020

International Journal of Distributed Sensor Networks

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A large number of smart devices make the Internet of Things world smarter. However, currently cloud computing cannot satisfy real-time requirements and fog computing is a promising technique for real-time processing. Operational modal analysis obtains modal parameters that reflect the dynamic properties of the structure from the vibration response signals. In Internet of Things, the operational modal analysis method can be embedded in the smart devices to achieve structural health monitoring and fault detection. In this article, a four-layer framework for combining fog computing and operational modal analysis in Internet of Things is designed. This four-layer framework introduces fog computing to solve tasks that cloud computing cannot handle in real time. Moreover, to reduce the time and space complexity of the operational modal analysis algorithm and support the real-time performance of fog computing, a limited memory eigenvector recursive principal component analysis–based operational modal analysis approach is proposed. In addition, by examining the cumulative percent variance of principal component analysis, this article explains the reasons behind the identified modal order exchange. Finally, the time-varying operational modal identification results from non-stationary random response signals of a cantilever beam whose density changes slowly indicate that the limited memory eigenvector recursive principal component analysis–based operational modal analysis method requires less memory and runtime and has higher stability and identification effect.

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An Introduction of a Robust OMA Method: CoS‐SSI and Its Performance Evaluation through the Simulation and a Case Study

Cited by 14 publications

References 18 publications

An update on techniques to assess normal-mode behavior of rock arches by ambient vibrations

An update on techniques to assess normal-mode behavior of rock arches by ambient vibrations

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

An online and real-time adaptive operational modal parameter identification method based on fog computing in Internet of Things

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