Mono-Component Feature Extraction for Condition Assessment in Civil Structures Using Empirical Wavelet Transform

Xia, Yunxia; Zhou, Yunlai

doi:10.3390/s19194280

Cited by 9 publications

(4 citation statements)

References 43 publications

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“…The success of this method is sensitive to parameter selection, as the choice of parameters can significantly impact the final recognition results. Reference [51] presented a feature extraction framework for underwater pulse signals based on morphology and wavelet packet transform, specifically for the field of ocean and underwater signal processing. This method has the capability of multiscale analysis and powerful spectral analysis, but its applicability may be limited to specific types of underwater pulse signals or specific application scenarios.…”

Section: Discussionmentioning

confidence: 99%

Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering

Han

Yuan

et al. 2023

Atmosphere

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Pulse signals refer to electromagnetic waveforms with short duration and high peak energy in the time domain. Spatial electromagnetic pulse interference signals can be caused by various factors such as lightning, arc discharge, solar disturbances, and electromagnetic disturbances in space. Pulse disturbance signals appear as instantaneous, high-energy vertical-line pulse trains (VLPTs) on the spectrogram. This paper uses computer vision techniques and unsupervised clustering algorithms to process and analyze VLPT on very-low-frequency (VLF) waveform spectrograms collected by the China Seismo-Electromagnetic Satellite (CSES) electric field detector. First, the waveform data are transformed into time–frequency spectrograms with a duration of 8 s using the short-time Fourier transform. Then, the spectrograms are subjected to grayscale transformation, vertical line feature extraction, and binarization preprocessing. In the third step, the preprocessed data are dimensionally reduced and fed into an unsupervised K-means++ clustering model to achieve automatic recognition and labeling of VLPTs. By recognizing and studying VLPT, not only can interference be recognized, but the temporal and spatial locations of these interferences can also be determined. This lays the foundation for identifying VLPT sources and gaining deeper insights into the generation, propagation, and characteristics of electromagnetic radiation.

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Section: Discussionmentioning

confidence: 99%

Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering

Han

Yuan

et al. 2023

Atmosphere

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“…Xin et al [14,15] used the EWT to extract the dense modal parameters of a pedestrian bridge and the time-varying modal parameters of a highway bridge. Xia and Zhou [16] employed scale-space EWT method to select mono-components from monitoring data, and obtain the instantaneous modal parameters and the linearity characteristics of the structures by the FREEVIB method.…”

Section: Introductionmentioning

confidence: 99%

Identification of bridge modal parameters from GNSS data by integrating IEWT and robust ICA algorithm

Yu,

Xie,

Fang

et al. 2024

Meas. Sci. Technol.

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Empirical Wavelet Transform (EWT) is often used to process GNSS bridge deformation monitoring data, but it leads to inaccurate band division and too many spurious modes. To address these problems, this study proposes an improved algorithm to identify bridge modal parameters, which combines improved EWT (IEWT) and Robust ICA. The proposed method adopts the autoregressive power spectrum of an improved covariance algorithm, instead of the Fourier spectrum, for band division. Additionally, it performs noise reduction and reconstruction of multi-channel GNSS monitoring data. The reconstructed signal is inputted as multi-channel observation signal into Robust ICA to extract features of the source signal. Finally, the natural excitation technique and Hilbert transform are used to solve the self-oscillation frequency and damping ratio of the structure. The proposed method is validated using both simulation data and the GNSS monitoring data of the Wilford suspension bridge. The results show that the proposed method can effectively reduce the measurement noise and successfully identify the first-order vibration frequencies and damping ratios of bridge. This algorithm can also be applied in the parameter identification of other engineering structures from GNSS data.

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“…Recently, several improvements or modifications have been proposed to overcome the shortcomings of traditional EWT. One way to improve the traditional EWT is employing a spectrum other than the Fourier, one which is used for an appropriate boundaries division, such as the pseudospectrum [24,25], power spectrum [26], scale-space representation [27], and time-frequency representation [28]. Amezquita-Sanchez et al [24,25] presented a pseudospectrum segment method based on multiple signal classification (MUSIC).…”

Section: Introductionmentioning

confidence: 99%

“…The enhanced EWT methods reliably identified the loosely spaced modes of a real footbridge and the instantaneous frequencies of a time-varying highway bridge. Xia et al [27] separated the mono-components from the health monitoring data of the civil structure via scale-space EWT and obtained the instantaneous modal parameters by the FREEVIB method. Another way to improve the traditional EWT is optimizing the Fourier spectrum segmentation method.…”

Section: Introductionmentioning

confidence: 99%

Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform

Fang

Meng

2021

Remote Sensing

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It is difficult to accurately identify the dynamic deformation of bridges from Global Navigation Satellite System (GNSS) due to the influence of the multipath effect and random errors, etc. To solve this problem, an improved empirical wavelet transform (EWT)-based procedure was proposed to denoise GNSS data and identify the modal parameters of bridge structures. Firstly, the Yule–Walker algorithm-based auto-power spectrum and Fourier spectrum were jointly adopted to segment the frequency bands of structural dynamic response data. Secondly, the improved EWT algorithm was used to decompose and reconstruct the dynamic response data according to a correlation coefficient-based criterion. Finally, Natural Excitation Technique (NExT) and Hilbert Transform (HT) were applied to identify the modal parameters of structures from the decomposed efficient components. Two groups of simulation data were used to validate the feasibility and reliability of the proposed method, which consisted of the vibration responses of a four-storey steel frame model, and the acceleration response data of a suspension bridge. Moreover, field experiments were carried out on the Wilford suspension bridge in Nottingham, UK, with GNSS and an accelerometer. The fundamental frequency (1.6707 Hz), the damping ratio (0.82%), as well as the maximum dynamic displacements (10.10 mm) of the Wilford suspension bridge were detected by using this proposed method from the GNSS measurements, which were consistent with the accelerometer results. In conclusion, the analysis revealed that the improved EWT-based method was capable of accurately identifying the low-order, closely spaced modal parameters of bridge structures under operational conditions.

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Mono-Component Feature Extraction for Condition Assessment in Civil Structures Using Empirical Wavelet Transform

Cited by 9 publications

References 43 publications

Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering

Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering

Identification of bridge modal parameters from GNSS data by integrating IEWT and robust ICA algorithm

Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform

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