A new method of vibration signal denoising based on improved wavelet

Miao, Feng; Zhao, Rongzhen

doi:10.1177/14613484211051857

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…The slow changing corresponds to the low-frequency part of the signal, representing the main contour of the signal; the fast changing corresponds to the high-frequency part of the signal, In our case, we use the DWT by Mallat algorithm. Which means is if an original signal f(t) is regarded as a discrete approximation with a resolution of 2 = 1 , it can be decomposed into approximation with a coarse resolution of 2 and successive detail approximation with a high resolution of 2 such that (2 < < ) [2].…”

Section: Design Of Wavelet Denoisermentioning

confidence: 99%

“…The advantage of denoising is to improve the signal to-noise ratio of the signal, so as to eliminate the false and retain the true. Fault diagnosis in electrical systems, the effect of noise elimination often directly affects the subsequent fault analysis and diagnosis [2][3][4][5]. In order to eliminate the noise in the signal, many methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

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Improving Performance of a DC-DC converter in a PV System using Wavelet-Discreet-Denoiser (WDD)

Abderahim,

Yassine,

Chrifi Alaoui

2024

Preprint

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This article deals with the denoising of signals from sensors (current and voltage) in a photovoltaic conversion chain using wavelet theory. The idea is to reduce the noise as much as possible without distorting or losing the original signal information. To see the reliability of this proposed method, a comparative study between our proposed method (WDD) and the classic low-pass filter method was presented. The contribution in this work is the improvement of the DC-DC boost converter via the use of wavelet theory, however, that’s why we set two objectives: the first is to obtain reliable control of the DC-DC boost converter via the proposed WDD and the second to increase the overall system efficiency. The experimental results show a very great improvement in the quality of the signals of the proposed method compared to the classic method of filtering by low-pass filter.

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Section: Design Of Wavelet Denoisermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Performance of a DC-DC converter in a PV System using Wavelet-Discreet-Denoiser (WDD)

Abderahim,

Yassine,

Chrifi Alaoui

2024

Preprint

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show abstract

“…Specifically, these methods are applicable to periodic signals or signals with clear demarcation points on the spectrum [8,17]. However, such methods may corrupt the phase information of the raw signals [18,19].…”

Section: Introductionmentioning

confidence: 99%

A receptive field transfer strategy via layer-aligned distillation learning for fault signal denoising

Pu,

Zhang,

2024

Meas. Sci. Technol.

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To improve fault diagnosis performance in complex noise environments, effective signal denoising techniques are necessary. However, traditional denoising methods have proven inadequate for multivariate fault signal denoising, neglecting the correlation among these signals. To this end, we propose a novel denoising module, inspired by traditional signal decomposition and reconstruction methods. Furthermore, to enhance the performance of proposed denoising module, we consider the influence of the receptive field and develop a receptive field transfer strategy using layer-aligned distillation learning. The experiments demonstrate that our approach effectively balances the denoising performance and computational load, offering a novel strategy for developing high-performance denoising networks. What's more, our strategy reduces the difficulty for fault diagnosis tasks under complex noise environments.

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“…Due to its adaptive decomposition characteristics, empirical mode decomposition (EMD) has been applied to nonlinear signal de-noising and achieved good de-noising effect, but there are still many problems to be solved, such as how to solve the modal aliasing and select the de-noising modal components [14]. All kinds of wavelet de-noising methods are also used in PD signal processing, and vibration signals of rotating machines, and in many cases, the effect is obviously better than other filtering methods, [15][16][17]. However, the effect of wavelet de-noising is often limited by wavelet function selection and parameter determination [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

De-noising of partial discharge ultrasonic signal of insulation bar in large motor based on GMC-wavelet

Chen

Zhang

et al. 2022

Journal of Electrical Engineering

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In view of the bad operation environment of large motor, which often suffers from various strong noise interference, the partial discharge ultrasonic signal is often annihilated, which makes it difficult to detect and analyse. A de-noising method based on generalized minimax concavity (GMC) and wavelet for partial discharge (PD) ultrasonic signal is proposed. GMC is used to enhance the sparsity of PD ultrasonic signal and eliminate the high-frequency noise signal at the same time. Then the residual high-frequency sparse noise and low-frequency noise of the former are de-noised again combined with wavelet. Finally, the signal is reconstructed to achieve the purpose of de-noising the original PD ultrasonic signal with noise. Compared with ℓ1 -norm method, GMC method, wavelet method and ℓ1 -norm-wavelet method, the simulation results show that based on time domain analysis, the de-noising effect of the proposed method is obviously better than the other four methods. The SNR and MSE of the former are better than those of the latter. In addition, the insulation bar discharge model of large motor is constructed to obtain the actual PD ultrasonic signal, which further verifies its effectiveness, and its de-noising effect is also better than the four methods. This method can not only enhance the sparsity of the target signal and improve the estimation accuracy, but also achieve the de-noising effect, while retaining the effective information of PD ultrasonic signal characteristics. This method can provide new ideas for other types of PD signal de-noising, and lay the foundation for later feature analysis.

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A new method of vibration signal denoising based on improved wavelet

Cited by 10 publications

References 21 publications

Improving Performance of a DC-DC converter in a PV System using Wavelet-Discreet-Denoiser (WDD)

Improving Performance of a DC-DC converter in a PV System using Wavelet-Discreet-Denoiser (WDD)

A receptive field transfer strategy via layer-aligned distillation learning for fault signal denoising

De-noising of partial discharge ultrasonic signal of insulation bar in large motor based on GMC-wavelet

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