Leak Location Based on PDS-VMD of Leakage-Induced Vibration Signal Under Low SNR in Water-Supply Pipelines

Li, Shuaiyong; Xia, Chuanqiang; Cheng, Zhenhua; Mao, Weipei; Liu, Ying; Habibi, Daryoush

doi:10.1109/access.2020.2984640

Cited by 34 publications

(12 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The specific solution method is as followswhere λ(t) are Lagrangian multipliers, α is the penalty factor, and we can adjust α to achieve a better decomposition result. After determining the appropriate penalty factor, we can use the alternating direction multiplier method (ADMM) to calculate the saddle point of equation (2), 22 which means that we can get the optimal solution in equation (1). Finally, all the eigenmode vectors are as followswhere the center frequency ωk can be obtained from formula (4)…”

Section: Variational Mode Decompositionmentioning

confidence: 99%

A novel bearing fault diagnosis method with feature selection and manifold embedded domain adaptation

Yang

Zheng

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

Traditional fault diagnosis models assume that the training and test data sets have the same feature distribution, but in practice the distribution between the training and test sets varies considerably, making it difficult to achieve the desired fault diagnosis performance. Thus, a diagnosis method based on feature selection and manifold embedding domain adaptation is proposed in this paper. First, the signal is decomposed by variational modal decomposition to obtain multiple modal components, and the entropy, time domain and frequency domain features of each modal component are extracted to form a mixed feature set. Second, it proposes a feature evaluation index based on Fisher scores and feature domain differences to select features that are conducive to cross-domain fault diagnosis and transfer learning. Then, the geodesic flow core is constructed to learn the transformation feature representation in the Grassmann manifold space to avoid features are distorted. Finally, the domain adaptation is performed by minimizing the discrepancy in the joint probability distribution between the same category and maximizing the discrepancy between the different categories. Based on the results of multi-index experiments, the method in this paper is superior to other existing methods.

show abstract

Section: Variational Mode Decompositionmentioning

confidence: 99%

A novel bearing fault diagnosis method with feature selection and manifold embedded domain adaptation

Yang

Zheng

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

“…In this study, VMD was used to preprocess the residual current signal. For VMD denoising, S. Li used the cross-correlation coefficient of each IMF and the original signal to screen the effective components [24], and X. Chen extracted the effective information according to the permutation entropy of each IMF to achieve signal denoising [25]. However, for weak signals, such as the residual current, it is difficult to effectively extract all the effective components based on a single indicator only.…”

Section: Vmd Noise Reductionmentioning

confidence: 99%

Residual Current Detection Method Based on Variational Modal Decomposition and Dynamic Fuzzy Neural Network

et al. 2021

View full text Add to dashboard Cite

To further improve the detection ability of residual current in low-voltage distribution networks, an adaptive residual current detection method based on variational mode decomposition (VMD) and dynamic fuzzy neural network (DFNN) is proposed. First, using the general K -value selection method of VMD proposed in this study, the residual current signal is decomposed into K intrinsic mode functions (IMFs). By introducing the cross-correlation coefficient R and the time-domain energy entropy ratio E as two classification indexes, IMFs are divided into three categories: effective IMFs, noise IMFs and aliasing IMFs. Then, the aliasing IMFs are denoised by recursive least squares (RLS), and the denoised IMFs are superimposed with the effective IMFs to obtain the reconstructed signal. Finally, the dynamic fuzzy neural network (DFNN) is adjusted by the minimum output method to achieve the detection of the reconstructed residual current signal, and the network is used to predict the residual current according to the detection results. The detection results of the simulation and measured data show that the proposed algorithm has high detection accuracy and is superior to the wavelet neural network, empirical mode decompositionthresholding, and wavelet entropy-auto encoder-back propagation neural network methods in terms of mean square error, goodness of fit and running time. This method provides a reference for further research on new adaptive residual current protection devices.

show abstract

“…VMD has been widely studied and applied in the denoising of vibration signals and the fault feature extraction of rotating machinery [24][25][26]. In this paper, VMD is introduced into the research of the NPW signal denoising and inflection point extraction, and the effectiveness of the proposed method is verified by simulation and actual experiments.…”

Section: Introductionmentioning

confidence: 99%

Application of VMD in Pipeline Leak Detection Based on Negative Pressure Wave

2021

View full text Add to dashboard Cite

Leakage problems are common in the water supply pipeline system, which will threaten the health of residents and cause economic losses. Negative pressure wave (NPW) technology calculates the time difference through the inflection point to locate the leak. However, due to the nonlinear and nonstationary characteristics of the pressure signal, it is difficult to obtain an accurate inflection point of the NPW by the traditional method. Therefore, the advantages of applying variational mode decomposition (VMD) in NPW technology are explored. Firstly, the correlation coefficient and permutation entropy (PE) are used for effective intrinsic mode function (IMF) component selection and parameter optimization. Thus, an adaptive denoising method based on VMD (AD-VMD) is presented. Then, to effectively separate the detail features of the NPW, a novel inflection point extraction method based on VMD (IPE-VMD) is proposed. Simulation and experimental results show that AD-VMD can effectively suppress noise interference and conserve the mutation characteristic of the leakage. IPE-VMD can obtain a distinct maximum peak at the inflection point and has good robustness to noise interference. This method can calculate the time difference precisely and stably. In addition, the accuracy of the leak location is verified. The average relative positioning error is 5.13%.

show abstract

Leak Location Based on PDS-VMD of Leakage-Induced Vibration Signal Under Low SNR in Water-Supply Pipelines

Cited by 34 publications

References 35 publications

A novel bearing fault diagnosis method with feature selection and manifold embedded domain adaptation

A novel bearing fault diagnosis method with feature selection and manifold embedded domain adaptation

Residual Current Detection Method Based on Variational Modal Decomposition and Dynamic Fuzzy Neural Network

Application of VMD in Pipeline Leak Detection Based on Negative Pressure Wave

Contact Info

Product

Resources

About