Denoising of UHF PD signals based on optimised VMD and wavelet transform

Long, Jiachuan; Wang, Xianpei; Dai, Dangdang; Tian, Meng; Guang, Zhu; Zhang, Jun

doi:10.1049/iet-smt.2016.0510

Cited by 50 publications

(41 citation statements)

References 19 publications

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“…The goal of VMD is to decompose a real valued input signal f into a discrete number of sub-signals (modes), x k , that have specific sparsity properties while reproducing the input. In terms of the data processing capabilities of the integrated model, it is mainly divided into the construction of the VMD model and its parsing process [27][28][29]. The processes of assessing the bandwidth are as follows: (1) compute the associated analytic signal by using the Hilbert transform in order to obtain a unilateral frequency spectrum, (2) shift the mode's frequency spectrum to "baseband" by mixing with an exponential tuned to the respective estimated center frequency, and (3) estimate the bandwidth using the Gaussian smoothness of the demodulated signal [30].…”

Section: Vmd-arima Modelmentioning

confidence: 99%

An Integrated Variational Mode Decomposition and ARIMA Model to Forecast Air Temperature

et al. 2019

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Temperature forecasting is a crucial part of climate change research. It can provide a valuable reference, as well as practical significance, for understanding the macroscopic evolutionary processes of regional temperature and for promoting sustainable development. This study presents a new integrated model, called the Variational Mode Decomposition-Autoregressive Integrated Moving Average (VMD-ARIMA) model, which reduces the required data input and improves the accuracy of predictions, based on the deficiencies of data dependence and the complicated mechanisms associated with current temperature forecasting. In this model, the variational mode decomposition (VMD) was used for mining the trend features and detailed features contained in a time series, as well as denoising. Moreover, the corresponding autoregressive integrated moving average (ARIMA) models were derived to reflect the different features of the components. The final forecasted values were then obtained using VMD reconstruction. The annual temperature time series from the Wuhan Meteorological Station were investigated using the VMD-ARIMA model, ARIMA model, and Grey Model (1, 1) based on three statistical performance metrics (mean relative error, mean absolute error, and root mean square error). The results indicate that the VMD-ARIMA model can effectively enhance the accuracy of temperature forecasting.

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Section: Vmd-arima Modelmentioning

confidence: 99%

An Integrated Variational Mode Decomposition and ARIMA Model to Forecast Air Temperature

et al. 2019

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“…However, the complicated and unpredictable electromagnetic environment in a substation may cause the PD signals to be contaminated by noise, leading to an incorrect diagnosis. Hence, a reliable noise suppression method is a prerequisite for the accurate detection and diagnosis of PD [8].…”

Section: Introductionmentioning

confidence: 99%

A New Denoising Method for UHF PD Signals Using Adaptive VMD and SSA-Based Shrinkage Method

Zhang

Long

et al. 2019

Sensors

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Noise suppression is one of the key issues for the partial discharge (PD) ultra-high frequency (UHF) method to detect and diagnose the insulation defect of high voltage electrical equipment. However, most existing denoising algorithms are unable to reduce various noises simultaneously. Meanwhile, these methods pay little attention to the feature preservation. To solve this problem, a new denoising method for UHF PD signals is proposed. Firstly, an automatic selection method of mode number for the variational mode decomposition (VMD) is designed to decompose the original signal into a series of band limited intrinsic mode functions (BLIMFs). Then, a kurtosis-based judgement rule is employed to select the effective BLIMFs (eBLIMFs). Next, a singular spectrum analysis (SSA)-based thresholding technique is presented to suppress the residual white noise in each eBLIMF, and the final denoised signal is synthesized by these denoised eBLIMFs. To verify the performance of our method, UHF PD data are collected from the computer simulation, laboratory experiment and a field test, respectively. Particularly, two new evaluation indices are designed for the laboratorial and field data, which consider both the noise suppression and feature preservation. The effectiveness of the proposed approach and its superiority over some traditional methods is demonstrated through these case studies.

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“…By comparison analysis, it is concluded that VMD overcomes the disadvantage of lacking theoretical basis and noise sensitivity of EMD when analyzing nonlinear and nonstationary signals. Based on the advantages of VMD method, it has been widely applied into fault diagnosis [11][12][13][14][15]. Long et al proposed a method combined VMD with WT [12] to reduce the strong background noise confusing in the raw signal and preserved the fault feature of raw signal effectively.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the advantages of VMD method, it has been widely applied into fault diagnosis [11][12][13][14][15]. Long et al proposed a method combined VMD with WT [12] to reduce the strong background noise confusing in the raw signal and preserved the fault feature of raw signal effectively. Variational mode decomposition and permutation entropy method was introduced in [15], which used VMD to extract the relative high-frequency mode components in raw vibration signal because the fault information in the vibration signal was mainly concentrated within the high-frequency components.…”

Section: Introductionmentioning

confidence: 99%

A Novel Detrended Fluctuation Analysis Method for Gear Fault Diagnosis Based on Variational Mode Decomposition

Xiao

2018

Shock and Vibration

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The vibration signal of heavy gearbox has the nonlinear and nonstationary characteristic, which makes the gear fault diagnosis difficult. Moreover, the useful fault information is mainly focused on the high-frequency components of the raw signal, which also affects the fault feature extraction from vibration signal. For this reason, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to diagnose the gear faults of heavy gearbox. Since high-frequency component contains more fault information, the raw vibration signal is decomposed several mode components by VMD, which can remove the low-frequency component to retain the high-frequency component. Moreover, the most sensitive mode component is selected in these high-frequency components by a maximal indicator, which is composed of kurtosis and correlation coefficient. The most sensitive mode component is calculated by DFA to obtain bi-logarithmic map, and the sliding windowing algorithm is employed to capture turning point of the bi-logarithmic map, thus extracting the fault feature of small time scale to identify gear faults. The effectiveness of the proposed method for fault diagnosis is validated by experimental data analysis, and the comparison results demonstrate that the recognition rate of gear faults condition have marked improvement by proposed method than the DFA of small time scale (STS-DFA) and EMD-DFA.

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Denoising of UHF PD signals based on optimised VMD and wavelet transform

Cited by 50 publications

References 19 publications

An Integrated Variational Mode Decomposition and ARIMA Model to Forecast Air Temperature

An Integrated Variational Mode Decomposition and ARIMA Model to Forecast Air Temperature

A New Denoising Method for UHF PD Signals Using Adaptive VMD and SSA-Based Shrinkage Method

A Novel Detrended Fluctuation Analysis Method for Gear Fault Diagnosis Based on Variational Mode Decomposition

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