Variational Mode Decomposition Based Time-Varying Force Identification of Stay Cables

Hou, Shitong; Dong, Bin; Fan, Jianhua; Wu, Gang; Wang, Haochen; Han, Yitian; Zhao, Xiaojin

doi:10.3390/app11031254

Cited by 15 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the cable forces can be obtained based on the fundamental frequencies of the mounted cables and the theory of structural dynamics. 47,48…”

Section: Experimental Validationmentioning

confidence: 99%

Data quality evaluation for bridge structural health monitoring based on deep learning and frequency-domain information

Deng

Zhong

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

Abnormal data recognition is of great importance in structural health monitoring. Most of the existing studies focused on detecting obvious abnormal data, which has obvious abnormal time-domain waveform. Pseudo normal data, which is normally looking in time domain but chaotic in frequency domain, did not receive enough attention and were likely to be misclassified as normal data. As a result, structural performance may be incorrectly evaluated because pseudo normal data are not recognized and eliminated from the monitoring data. This study developed a novel quality evaluation framework for monitoring data of bridge dynamic response. The main novelty of the proposed framework is that the frequency-domain information is used to characterize the quality of the monitoring data so that the normal data, obvious abnormal data, and pseudo normal data can be accurately distinguished. In the framework, the frequency-domain information was obtained by fast Fourier transform (FFT), and the Gramian angular field images converted from FFT results were used to train a designed convolutional neural network (CNN). The cable acceleration data of the Waitan cable-stayed bridge were taken as an example to verify the accuracy of the proposed framework. Compared with the CNN models based on time-domain images and time-frequency stacked images, this framework can better recognize pseudo normal data from the monitoring data. In large-scale testing, the classification accuracy of all channels was more than 96%. Finally, two cable acceleration sensors of another cable-stayed bridge were used to demonstrate the feasibility of the framework in cross-object application. The results show that the framework has good accuracy and robustness in large-scale monitoring data quality evaluation and cross-object application.

show abstract

“…Furthermore, the cable forces can be obtained based on the fundamental frequencies of the mounted cables and the theory of structural dynamics. 47,48…”

Section: Experimental Validationmentioning

confidence: 99%

Data quality evaluation for bridge structural health monitoring based on deep learning and frequency-domain information

Deng

Zhong

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…Some attempts have been conducted to extract the time-varying cable force from the dynamic response of a cable [4,[12][13][14][15][16][17][18][19][20][21][22][23]. The most commonly used method is to estimate the instantaneous frequency of the cable response firstly and then calculate the time-varying cable force according to the tension string principle [4,[12][13][14][15][16][17][18][19]. Enhancing the resolution of the time-frequency analysis methods both in the time and frequency domains is their kernel.…”

Section: Introductionmentioning

confidence: 99%

“…However, the frequency resolution of WT needs to be improved and the boundary effect of WT is problematic [24,25]. Hou et al [19] provided a method based on the variational mode decomposition (VMD). On-site monitoring of the cable force was conducted to illustrate the validity of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Identification of Time-Varying Cable Force Using an Improved Adaptive Extended Kalman Filter

Yang

et al. 2022

Sensors

View full text Add to dashboard Cite

The real-time identification of time-varying cable force is critical for accurately evaluating the fatigue damage of cables and assessing the safety condition of bridges. In the context of unknown wind excitations and only one available accelerometer, this paper proposes a novel cable force identification method based on an improved adaptive extended Kalman filter (IAEKF). Firstly, the governing equation of the stay cable motion, which includes the cable force variation coefficient, is expressed in the modal domain. It is transformed into a state equation by defining an augmented Kalman state vector with the cable force variation coefficient concerned. The cable force variation coefficient is then recursively estimated and closely tracked in real time by the proposed IAEKF. The contribution of this paper is that an updated fading-factor matrix is considered in the IAEKF, and the adaptive noise error covariance matrices are determined via an optimization procedure rather than by experience. The effectiveness of the proposed method is demonstrated by the numerical model of a real-world cable-supported bridge and an experimental scaled steel stay cable. Results indicate that the proposed method can identify the time-varying cable force in real time when the cable acceleration of only one measurement point is available.

show abstract

“…From this, a time-frequency distribution that can clearly and accurately reflect the distribution of signal energy on various scales in space is obtained, which is conducive to more detailed analysis of signal characteristics. There are many documents on the application of VMD in different fields, and it has a lot of contributions [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

The Application of Machine Learning ICA-VMD in an Intelligent Diagnosis System in a Low SNR Environment

Lin

2021

Sensors

View full text Add to dashboard Cite

This paper proposes a new method called independent component analysis–variational mode decomposition (ICA-VMD), which combines ICA and VMD. The purpose is to study the application of ICA-VMD in low signal-to-noise ratio (SNR) signal processing and data analysis. ICA is a very important method in the field of machine learning. It is an unsupervised learning algorithm that can dig out the independent factors hidden in the observation signal. The VMD method estimates each signal component by solving the frequency domain variational optimization problem, and it is very suitable for mechanical fault diagnosis. The advantage of ICA-VMD is that it requires two sensory cues to distinguish the original source from the unwanted noise. In the three cases studied here, the original source was first contaminated by white Gaussian noise. The three cases in this study are under different SNR conditions. The SNR in the first case is –6.46 dB, the SNR in the second case is –21.3728, and the SNR in the third case is –46.8177. The simulation results show that the ICA-VMD method can effectively recover the original source from the contaminated data. It is hoped that, in the future, there will be new discoveries and advances in science and technology to solve the noise interference problem through this method.

show abstract

Variational Mode Decomposition Based Time-Varying Force Identification of Stay Cables

Cited by 15 publications

References 28 publications

Data quality evaluation for bridge structural health monitoring based on deep learning and frequency-domain information

Data quality evaluation for bridge structural health monitoring based on deep learning and frequency-domain information

Real-Time Identification of Time-Varying Cable Force Using an Improved Adaptive Extended Kalman Filter

The Application of Machine Learning ICA-VMD in an Intelligent Diagnosis System in a Low SNR Environment

Contact Info

Product

Resources

About