Structural system identification based on variational mode decomposition

Bagheri, Abdollah; Ozbulut, Osman E.; Harris, Devin K.

doi:10.1016/j.jsv.2017.12.014

Cited by 84 publications

(30 citation statements)

References 38 publications

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“…In this regard, modal parameter identification (MPI), especially natural frequencies and damping ratios, of civil structures has demonstrated to be critical because these parameters can reflect vibration characteristics (Rainieri et al, 2018). MPI has been extensively applied in damage detection (Magalhães et al, 2012), modal updating (Bagheri et al, 2018), vibration control (Bakule et al, 2016), and seismic design (Muho et al, 2019). Because operational modal analysis is practical and inexpensive, an increasing number of researchers are exploring techniques using ambient vibration (Amezquita-Sanchez et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, closely spaced modes would not be well separated by the EWT. Recently, variational mode decomposition (VMD), which was proposed by Dragomiretskiy and Zosso (2014), was applied to MPI by Bagheri et al (2018). The VMD method decomposes a signal into a set of distinctive subsignals with a center frequency contained in each subsignal.…”

Section: Introductionmentioning

confidence: 99%

“…Frequency domain decomposition (Brincker et al, 2001), the peakpicking method (Ren et al, 2004), and enhanced frequency domain decomposition (Jacobsen et al, 2006) are examples of frequency domain algorithms. Frequency domain algorithms have a few common limitations; for example, they are not suitable for signals with a low signal-to-noise ratio (SNR) and closely spaced modes (Bagheri et al, 2018). Many time-domain algorithms exist, such as stochastic subspace identification (SSI; Peeters and De Roeck, 1999), the natural excitation technique combined with the eigensystem realization algorithm (NExT-ERA; Caicedo et al, 2004), and the random decrement technique combined with the Ibrahim time domain (RDT-ITD; Ibrahim, 1977;Ibrahim and Pappa, 1982).…”

Section: Introductionmentioning

confidence: 99%

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Modal parameter identification of structures based on short-time narrow-banded mode decomposition

Zhou

Zhang

et al. 2020

Advances in Structural Engineering

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The accurate estimation of natural frequencies and damping ratios is critical for civil structures. In this article, a method based on short-time narrow-banded mode decomposition is proposed to analyze the modal parameters of civil structures. In this approach, short-time narrow-banded mode decomposition is applied to identify time-varying structures with free vibration responses. On the contrary, by analysis of the weighting factors α and β, short-time narrow-banded mode decomposition is improved to estimate the parameter of time-invariant systems. In the case of enhanced short-time narrow-banded mode decomposition, the original short-time narrow-banded mode decomposition approach is modified in two ways. First, the instantaneous frequency term of the objective function is removed, and one weighting factor remains, that is, α in the objective function. Second, a technique is provided to automatically detect the optimum value of α. Two numerical examples, that is, a three-degree-of-freedom time-variant system and a simple model of the Lysefjord bridge are provided. In addition, an experiment with a real-life pedestrian bridge located at Tufts University, United States, is used to demonstrate the applicability of the proposed method. The analysis results indicate that the proposed method can easily identify high-quality natural frequencies and damping ratios.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modal parameter identification of structures based on short-time narrow-banded mode decomposition

Zhou

Zhang

et al. 2020

Advances in Structural Engineering

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“…VMD, as an instrumental tool to extract valid information from erratic signals, was proposed by Dragomiretskiy and Zosso [38]. It has been continually applied in many fields, including forecasting [39], structural system identification [40] and vibration fault diagnosis [41], and has proved to be more robust relative to EMD and EEMD in alleviating spurious components and modal aliasing. In the process of decomposition, the original sequence was decomposed into several modes, and the modes were acquired from every decomposition consistent with the steps below:…”

Section: A Variational Mode Decomposition (Vmd)mentioning

confidence: 99%

Wind Speed Forecasting Using a Two-Stage Forecasting System With an Error Correcting and Nonlinear Ensemble Strategy

2019

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Precise wind speed prediction is increasingly practical for sustained and stable wind energy utilization considering the growing portion of wind energy in the global electric grid. Although plenty of wind speed forecasting approaches have been devoted to improving forecasting performance, the majority have neglected the utilization of error information, integrated the forecasting value of every component with simple ensemble approaches, and ignored forecasting stability, which may make the forecasting results poor. Considering the above drawbacks, a two-stage forecasting system is performed in our study based on the data preprocessing approach, improved multi-objective optimization algorithm, error correction and nonlinear ensemble strategy. The developed system effectively overcomes the shortcomings of previous models and observably prompts wind speed forecasting capacity. For investigating the prediction capacity of the developed system, six wind speed series obtained from China and Spain are applied as case sites. The forecasting consequences indicate that the developed system is more conducive to enhancing forecasting precision and stability than other involved models, which can provide beneficial assistance for wind speed forecasting. INDEX TERMS Wind speed forecasting, multi-objective optimization algorithm, ensemble strategy, forecasting accuracy.

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“…Structural acceleration responses can be obtained as the superposition of P vibration modal responses [31], and when an impulse force is applied on the p-th dof, the acceleration response of the k-th generalized modal coordinate is given as…”

Section: Instantaneous Modal Parametermentioning

confidence: 99%

Instantaneous Modal Parameter Identification of Linear Time-Varying Systems Based on Chirplet Adaptive Decomposition

Zhang

Shi

2019

Shock and Vibration

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Instantaneous modal parameter identification of time-varying dynamic systems is a useful but challenging task, especially in the identification of damping ratio. This paper presents a method for modal parameter identification of linear time-varying systems by combining adaptive time-frequency decomposition and signal energy analysis. In this framework, the adaptive linear chirplet transform is applied in time-frequency analysis of acceleration response for its higher energy concentration, and the response of each mode can be adaptively decomposed via an adaptive Kalman filter. Then, the damping ratio of the time-varying systems is identified based on energy analysis of component response signal. The proposed method can not only improve the accuracy of instantaneous frequency extraction but also ensure the antinoise ability in identifying the damping ratio. The efficiency of the method is first verified through a numerical simulation of a three-degree-of-freedom time-varying structure. Then, the method is demonstrated by comparing with the traditional wavelet and time-domain peak method. The identified results illustrate that the proposed method can obtain more accurate modal parameters in low signal-to-noise ratio (SNR) scenarios.

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Structural system identification based on variational mode decomposition

Cited by 84 publications

References 38 publications

Modal parameter identification of structures based on short-time narrow-banded mode decomposition

Modal parameter identification of structures based on short-time narrow-banded mode decomposition

Wind Speed Forecasting Using a Two-Stage Forecasting System With an Error Correcting and Nonlinear Ensemble Strategy

Instantaneous Modal Parameter Identification of Linear Time-Varying Systems Based on Chirplet Adaptive Decomposition

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