Identification of modal parameters from noisy transient response signals

He, Dan; Wang, Xiufeng; Friswell, Michael I.; Lin, Jing

doi:10.1002/stc.2019

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…For vibration signals submerged in strong background noise, signal denoising methods by using Wiener filter, 7 wavelet filter, 8 and singular value decomposition (SVD) 9 have been commonly used to reduce the influence of noise and improve the quality of time frequency analysis. In fact, most of the abovementioned methods remove noise in the frequency domain and are only suitable for signals with energy compact spectra.…”

Section: Introductionmentioning

confidence: 99%

High‐resolution time‐frequency representation for instantaneous frequency identification by adaptive Duffing oscillator

Peng

Hao

et al. 2020

Struct Control Health Monit

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Time-frequency analysis of structural vibration responses provides essential information for structural system identification, modal updating, and condition assessment. However, spurious peaks introduced by the strong noise will significantly increase the false positive rate as well as compromise the sparse time-frequency signal representation. This paper proposes a high-resolution time-frequency representation approach for nonstationary signals polluted with strong noise. With a high sensitivity to detect slight frequency shift and an immunity to noise effect, the intermittent chaotic of Duffing oscillator system is introduced to accurately identify the time-varying instantaneous frequencies. Furthermore, an adaptive Duffing oscillator array is adopted to improve the instantaneous frequency identification efficiency. The feasibility and effectiveness of the proposed method are verified with numerical and experimental studies. Numerical studies are conducted on a multicomponent synthetic nonstationary signal as well as on a two-story shear building with time-varying stiffness under seismic loads. In experimental validations, the acceleration response of a laboratory bridge model under moving vehicle load is also analyzed by using the proposed approach to obtain the instantaneous frequency variations induced by the bridge-vehicle interaction. These results are compared with those obtained from a method based on empirical wavelet transform and Hilbert transform (EWT-HT), to highlight the superiority of the proposed approach in obtaining high-resolution time-frequency analysis results for nonstationary signals with strong noise.

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Section: Introductionmentioning

confidence: 99%

High‐resolution time‐frequency representation for instantaneous frequency identification by adaptive Duffing oscillator

Peng

Hao

et al. 2020

Struct Control Health Monit

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“…In modal testing, the measured FRF signals are usually contaminated with noise [1,2], and the noise will interfere with the accurate extraction of modal features. To eliminate the noise and purify the measured FRF signals, the noise reduction method should be exploited.…”

Section: Introductionmentioning

confidence: 99%

Noise Reduction for Modal Parameter Identification of the Measured FRFs Using the Modal Peak-Based Hankel-SVD Method

Zhu

Zang

Zhang

2020

Shock and Vibration

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The measured frequency response functions (FRFs) in the modal test are usually contaminated with noise that significantly affects the modal parameter identification. In this paper, a modal peak-based Hankel-SVD (MPHSVD) method is proposed to eliminate the noise contaminated in the measured FRFs in order to improve the accuracy of the identification of modal parameters. This method is divided into four steps. Firstly, the measured FRF signal is transferred to the impulse response function (IRF), and the Hankel-SVD method that works better in the time domain rather than in the frequency domain is further applied for the decomposition of component signals. Secondly, the iteration of the component signal accumulation is conducted to select the component signals that cover the concerned modal features, but some component signals of the residue noise may also be selected. Thirdly, another iteration considering the narrow frequency bands near the modal peak frequencies is conducted to further eliminate the residue noise and get the noise-reduced FRF signal. Finally, the modal identification method is conducted on the noise-reduced FRF to extract the modal parameters. A simulation of the FRF of a flat plate artificially contaminated with the random Gaussian noise and the random harmonic noise is implemented to verify the proposed method. Afterwards, a modal test of a flat plate under the high-temperature condition was undertaken using scanning laser Doppler vibrometry (SLDV). The noise reduction and modal parameter identification were exploited to the measured FRFs. Results show that the reconstructed FRFs retained all of the modal features we concerned about after the noise elimination, and the modal parameters are precisely identified. It demonstrates the superiority and effectiveness of the approach.

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“…Furthermore, these methods require the time series data to cover more than three periods of the lowest frequency signal component, and the extracted frequencies are the averaged values (not instantaneous) of all the sampled sections. [38][39][40][41][42][43] Therefore, a new real-time frequency identification method is necessary.…”

Section: Introductionmentioning

confidence: 99%

An instantaneous frequency analysis method of stay cables

Zhong

Pai

2019

Journal of Low Frequency Noise, Vibration and Active Control

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This paper presents and evaluates in detail a conjugate-pair decomposition method for tracking the instantaneous frequency of a vibration signal by processing only three (or more) most recent data points, and Probability Box theory was employed to obtain the uncertain probability distribution of instantaneous frequency. A composite cablestayed bridge with large span, named as Guanhe Bridge, was selected as the engineering background; the proposed conjugate-pair decomposition method is verified and compared with Hilbert-Huang transform method using the measured dynamical data of structural health monitoring system. Moreover, the uncertain probability distribution of instantaneous frequency was discussed; the Probability Box also can be obtained. Results show that the identify results are close to each other using conjugate-pair decomposition and Hilbert-Huang transform, respectively; but the variance of conjugate-pair decomposition is less than Hilbert-Huang transform, and conjugate-pair decomposition requires only a few data points for sliding-window fitting to extract the instantaneous frequency, and the Probability Box of instantaneous frequency will be useful to evaluate safety and potential fatigue problems in the bridge cables.

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Identification of modal parameters from noisy transient response signals

Cited by 4 publications

References 21 publications

High‐resolution time‐frequency representation for instantaneous frequency identification by adaptive Duffing oscillator

High‐resolution time‐frequency representation for instantaneous frequency identification by adaptive Duffing oscillator

Noise Reduction for Modal Parameter Identification of the Measured FRFs Using the Modal Peak-Based Hankel-SVD Method

An instantaneous frequency analysis method of stay cables

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