Structural response reconstruction based on empirical mode decomposition in time domain

He, Jingjing; Guan, Xuefei; Liu, Yongming

doi:10.1016/j.ymssp.2011.12.010

Cited by 95 publications

(42 citation statements)

References 24 publications

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“…Similarly, the obtained reconstruction responses are averaged. Figure 18 shows measurement strain data and reconstructed strain responses of the 5-th optical fiber measurement point in the first 4 s. From Figure 18, it is observed that increasing the number of the measurement points has trivial From Figure 18, it is observed that increasing the number of the measurement points has trivial effects on the accuracy of the reconstruction results, which is consistent with the simulation results in [14]. However, in the real situation, increasing the number of measurement points can improve the robustness of the data acquisition in case of measurement points are broken or record incorrect data.…”

Section: Case 1 Effect Of Sensor Numbersupporting

confidence: 77%

“…The reconstructed strain response at the 15-th measurement point and the theoretical result are compared in Figure 17a. Figure 17b boundary/end effect in EMD method [14], which has been widely discussed as the intrinsic weakness of EMD. One of the possible solutions for this problem is using the mirroring method to extend signal envelopes from both ends, thus effectively eliminating end effects.…”

Section: Resultsmentioning

confidence: 99%

“…For clear demonstration, the middle portion (4-6 s) data are shown. The four modal response results are used in Equation (13) Correlation coefficients between the reconstructed strain responses and the theoretical strain responses for the three locations are 0.981, 0.978, and 0.981, respectively, indicating that the proposed methodology produces reliable and accurate results over the entire structure.To reconstruct the stress responses, the reconstructed strain responses are used in Equation (14). The material matrix for this 1D beam problem is a 1 × 1 matrix (a scalar) of…”

Section: Example 1: a Numerical Beam Structure Examplementioning

confidence: 99%

“…Figure 20b shows measured strain responses and reconstructed strain responses in 1.5~2 s for clear presentation. in [14]. However, in the real situation, increasing the number of measurement points can improve the robustness of the data acquisition in case of measurement points are broken or record incorrect data.…”

Section: Case 2 Effect Of Sensor Locationmentioning

confidence: 99%

See 3 more Smart Citations

Time Domain Strain/Stress Reconstruction Based on Empirical Mode Decomposition: Numerical Study and Experimental Validation

Zhou

Guan

et al. 2016

Preprint

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Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.

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Section: Case 1 Effect Of Sensor Numbersupporting

confidence: 77%

Section: Resultsmentioning

confidence: 99%

Section: Example 1: a Numerical Beam Structure Examplementioning

confidence: 99%

Section: Case 2 Effect Of Sensor Locationmentioning

confidence: 99%

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Time Domain Strain/Stress Reconstruction Based on Empirical Mode Decomposition: Numerical Study and Experimental Validation

Zhou

Guan

et al. 2016

Preprint

Self Cite

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“…Data recovery can be considered as a remedy when the data loss occurs. To address the lost data recovery issue in SHM, approaches based on the finite element model (FEM) have been proposed . Other than those model‐based methods that demand an accurate FEM, response recovery conducted directly from the measurements is more attractive and applicable for large‐scale infrastructure with numerous uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Lost data recovery for structural health monitoring based on convolutional neural networks

Gao

Hao

2019

Struct Control Health Monit

117

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Summary Signal transmission loss of using wireless sensors for structural health monitoring is a usual case, which undermines the reliability of the sensors for monitoring the structural conditions. The measured vibration data with a high data loss ratio can hardly be used for the analysis, that is, modal identification, as it will lead to significant errors in the results. This paper proposes a novel approach based on convolutional neural networks for recovering the lost vibration data for structural health monitoring. The used network is a fully feed‐forward convolutional neural network with bottleneck architecture and skip connection, which constructs the nonlinear relationships between the incomplete signal with data loss measured from the sensors with the transmission loss and the complete true signal. The trained network extracts the robust higher representation features of the measured incomplete signals using the compression layers and expands those features gradually throughout the reconstruction layers to recover and obtain the complete true signals. The long‐term vibration data from Dowling Hall Footbridge are employed to validate the effectiveness and robustness of the proposed approach for the lost data recovery. Two case studies are conducted to validate the recovery accuracy for single‐channel and multiple‐channel cases, respectively. The effect of sampling rate on the recovery accuracy is also investigated. The proposed approach exhibits the outstanding capability of lost data recovery, even when the signals have severe data loss ratios up to 90%. To further demonstrate the reliability of the recovered signals for data analysis, modal identification results by using the recovered signals with different data loss ratios show a very good agreement with those obtained from the complete true data.

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Multi‐channel response reconstruction using transformer based generative adversarial network

Zheng

et al. 2023

Earthq Engng Struct Dyn

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Accurate measurement data are a basic prerequisite for effective structural health monitoring (SHM). However, data loss are inevitable in the long‐term monitoring of large‐scale structures. To solve this problem, this research proposes a transformer‐based generative adversarial network (GAN) to reconstruct lost measurements from observed measurements. The generator of GAN is an encoder‐decoder structure using transformer as the backbone combined with discrete wavelet transform. Skip connections are used between the encoder part and decoder part to promote multi‐scale information flow. A novel discriminator is designed to assess the reality of wavelet spectra of reconstructed samples. To deceive the discriminator, the generator must generate samples that are accurate over the full frequency band. The developed model is used to reconstruct linear responses of a footbridge under pedestrian excitations and nonlinear responses of a suspension bridge under typhoon events. Experimental results demonstrate that lost responses can be reconstructed accurately, even when a large proportion of data are lost. The effectiveness of the proposed method is further verified by comparing the reconstruction accuracy of the proposed model with those of other three state‐of‐the‐art models. The results demonstrate that an improved performance of applying the proposed approach for dynamic structural response reconstruction is achieved and validated with in‐field testing data under ambient and extreme excitation conditions.

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Structural response reconstruction based on empirical mode decomposition in time domain

Cited by 95 publications

References 24 publications

Time Domain Strain/Stress Reconstruction Based on Empirical Mode Decomposition: Numerical Study and Experimental Validation

Time Domain Strain/Stress Reconstruction Based on Empirical Mode Decomposition: Numerical Study and Experimental Validation

Lost data recovery for structural health monitoring based on convolutional neural networks

Multi‐channel response reconstruction using transformer based generative adversarial network

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