A Denoising Method of Partial Discharge Signal Based on Improved SVD-VMD

Lei, Zhipeng; Wang, Feiyu; Li, Chuanyang

doi:10.1109/tdei.2023.3269725

Cited by 22 publications

(7 citation statements)

References 28 publications

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“…The success of ELM in GWL prediction compared to other algorithms (ANN, SVR and RBF) has also been emphasized by other studies in the literature (Alizamir et al, 2018;Yadav et al, 2017). Gong et al dominant modes, thereby improving the prediction accuracy of GWL (Lei et al, 2023;Thapa & Kayastha, 2015;Zaini et al, 2022). This study is one of the first applications to enhance the frequency mode separation and improve GWL prediction accuracy by decomposing the highest frequency IMF1 component obtained through EEMD into sub-bands using VMD algorithm.…”

Section: Performance Of Hybrid Models In Gwl Forecastingmentioning

confidence: 77%

Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

Gezici,

Katipoğlu,

Şengül

2024

Hydrological Processes

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Water scarcity is a pressing issue, intensified by factors such as population growth and industrialization. Hence, it is crucial to monitor, conserve and analyse groundwater resources, which are essential sources of clean and usable water. This study examines changes in groundwater levels (GWLs) in northeastern Turkey's mountainous and snow‐covered area. The primary objective is to assess the effectiveness of integrated machine learning models, specifically, the extreme learning machine (ELM) technique combined with signal decomposition techniques such as ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD) and empirical wavelet transform (EWT) for monthly GWL prediction models. Seventy percent of the accessible data is allocated for training, while 30% is designated for testing. A correlation matrix involving precipitation, temperature, relative humidity and GWL parameters is generated with inputs that possess significant correlations being selected, such as GWLt−1, GWLt−2, RHt, RHt−1 and RHt−2. To evaluate model results, various metrics, including mean squared error, mean absolute error, mean absolute percentage error, mean bias error, bias factor, determination coefficient, Nash‐Sutcliffe efficiency, as well as tools such as box plots, Taylor diagrams and radar charts, are utilized to compare outcomes during the interpretation phase. The results of the analyses show that applying data decomposition methods such as EEMD, VMD and EWT significantly improves the performance of the ELM algorithm in predicting GWLs. VMD‐ELM is the most accurate for GWL forecasting among the approaches examined. The R2 values of the most successful models established in the two wells are 0.993 and 0.905. The outcomes of this research hold significance for decision‐makers and policymakers as it offers informative insights into aquifer surveillance, irrigation strategizing and efficient administration of water resources.

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Section: Performance Of Hybrid Models In Gwl Forecastingmentioning

confidence: 77%

Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

Gezici,

Katipoğlu,

Şengül

2024

Hydrological Processes

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“…Drawing upon the principles of phase space reconstruction, it is feasible to reconstruct the Hankel matrix as follows [26][27][28]:…”

Section: Singular Value Decompositionmentioning

confidence: 99%

Research on Feature Extraction and Fault Diagnosis Method for Rolling Bearing Vibration Signals Based on Improved FDM-SVD and CYCBD

Yang

2024

Symmetry

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In mechanical equipment, rolling bearing components are constantly exposed to intricate and diverse environmental conditions, rendering them vulnerable to wear, performance degradation, and potential malfunctions. To precisely extract and discern rolling bearing vibration signals amidst intricate noise interference, this paper introduces a fault feature extraction and diagnosis methodology that seamlessly integrates an improved Fourier decomposition method (FDM), singular value decomposition (SVD), and maximum second-order cyclostationary blind convolution (CYCBD). Initially, the FDM is employed to meticulously decompose the bearing fault signals into numerous signal components. Subsequently, a comprehensive weighted screening criterion is formulated, aiming to strike a balance between multiple indicators, thereby enabling the selective screening and reconstruction of pertinent signal components. Furthermore, SVD and CYCBD techniques are introduced to carry out intricate processing and envelope demodulation analysis of the reconstructed signals. Through rigorous simulation experiments and practical rolling bearing fault diagnosis tests, the method’s noteworthy effectiveness in suppressing noise interference, enhancing fault feature information, and efficiently extracting fault features is unequivocally demonstrated. Furthermore, compared to traditional time–frequency analysis methods such as EMD, EEMD, ITD, and VMD, as well as traditional deconvolution methods like MED, OMEDA, and MCKD, this method exhibits significant advantages, providing an effective solution for diagnosing rolling bearing faults in environments with strong background noise.

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“…This paper compares Method A with the other three common PD denoise methods-AEEMD [10], the integration of SVD with VMD algorithm [11], and the Adaptive Wavelet Multilevel Soft Threshold algorithm [12]-on different input SNR PD signals to demonstrate the noise reduction effect. For example, when the input SNR is 7.76 dB, the signals after de-noising can be obtained as in Figure 11.…”

Section: Comparation Of the Denoising Effectmentioning

confidence: 99%

“…SVD is also always applied for noise suppression, but it requires manual determination of effective singular value orders, making the process susceptible to human error [9]. In recent research on denoising PD signals, researchers have increasingly turned to advanced and hybrid versions of traditional algorithms such as the AEEMD algorithm [10], the integration of SVD with the VMD algorithm [11], and the Adaptive Wavelet Multilevel Soft Threshold algorithm [12].…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Signal Denoising Algorithm Based on Aquila Optimizer–Variational Mode Decomposition and K-Singular Value Decomposition

Zhong,

Liu,

2024

Applied Sciences

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Partial discharge (PD) is a primary factor leading to the deterioration of insulation in electrical equipment. However, it is hard for traditional methods to precisely extract PD signals in increasingly complex engineering environments. This paper proposes a new PD signal denoising method combining Aquila Optimizer–Variational Mode Decomposition (AO-VMD) and K-Singular Value Decomposition (K-SVD) algorithms. Firstly, the AO algorithm optimizes critical parameters of the VMD algorithm. For the PD signal overwhelmed by noise, the AO-VMD algorithm can decompose it and reconstruct it by using kurtosis. In this process, the majority of the noise is removed, and the characteristics of the original signal are shown. Subsequently, the K-SVD algorithm performs sparse decomposition on the signal after OA-VMD, constructs a learned dictionary, and captures the characteristics of the signal for continuous learning and updating. After the dictionary learning is completed, the best matching atoms from the dictionary are selected to precisely reconstruct the original noiseless signal. Finally, the proposed method is compared with three traditional algorithms, Adaptive Ensemble Empirical Mode Decomposition (AEEMD), SVD-VMD, and the Adaptive Wavelet Multilevel Soft Threshold algorithm, on the simulated signal and the actual engineering signal. The results both demonstrate that the algorithm proposed by this paper has superior noise reduction and signal extraction performance.

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A Denoising Method of Partial Discharge Signal Based on Improved SVD-VMD

Cited by 22 publications

References 28 publications

Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

Research on Feature Extraction and Fault Diagnosis Method for Rolling Bearing Vibration Signals Based on Improved FDM-SVD and CYCBD

Partial Discharge Signal Denoising Algorithm Based on Aquila Optimizer–Variational Mode Decomposition and K-Singular Value Decomposition

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