Denoising Vibration Signals of Food Refrigerant Air Compressor Based on Wavelet Transform

2010 ◽  
Vol 439-440 ◽  
pp. 1037-1041 ◽  
Author(s):  
Yan Jue Gong ◽  
Zhao Fu ◽  
Hui Yu Xiang ◽  
Li Zhang ◽  
Chun Ling Meng
Keyword(s):  
Wavelet Transform ◽  
Frequency Characteristic ◽  
Vibration Signal ◽  
Wavelet Denoising ◽  
Signal Denoising ◽  
Denoising Method ◽  
Vibration Signals ◽  
Air Compressor ◽  
Time Frequency ◽  
Soft Threshold

On the basis of wavelet denoising and its better time-frequency characteristic, this paper presents an effective vibration signal denoising method for food refrigerant air compressor. The solution of eliminating strong noise is investigated with the combination of soft threshold and exponential lipschitza. The good denoising results show that the presented method is effective for improving the signal noise ratio and builds the good foundation for further extraction of the vibration signals.

A new method of vibration signal denoising based on improved wavelet

2021 ◽  
pp. 146134842110518
Author(s):  
Feng Miao ◽  
Rongzhen Zhao
Keyword(s):  
Wavelet Transform ◽  
Wavelet Decomposition ◽  
Vibration Signal ◽  
Noise Cancellation ◽  
New Method ◽  
Signal Denoising ◽  
Denoising Method ◽  
Noise Component ◽  
Vibration Signals ◽  
The Given

Noise cancellation is one of the most successful applications of the wavelet transform. Its basic idea is to compare wavelet decomposition coefficients with the given thresholds and only keep those bigger ones and set those smaller ones to zero and then do wavelet reconstruction with those new coefficients. It is most likely for this method to treat some useful weak components as noise and eliminate them. Based on the cyclostationary property of vibration signals of rotating machines, a novel wavelet noise cancellation method is proposed. A numerical signal and an experimental signal of rubbing fault are used to test and compare the performances of the new method and the conventional wavelet based denoising method provided by MATLAB. The results show that the new noise cancellation method can efficaciously suppress the noise component at all frequency bands and has better denoising performance than the conventional one.

The Study of Signal Denoising for Laser Cutting Based on Adaptive Wavelet Denoising

2012 ◽  
Vol 546-547 ◽  
pp. 686-690
Author(s):  
Hui Juan Hao ◽  
Ji Yong Xu ◽  
Juan Li
Keyword(s):  
Laser Cutting ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Denoising ◽  
Quality Analysis ◽  
Signal Denoising ◽  
Threshold Method ◽  
Denoising Method ◽  
Swarm Optimization ◽  
Adaptive Wavelet ◽  
Soft Threshold

In order to reduce the noise of acquisition signal in laser cutting, an adaptive wavelet denoising method is proposed in this paper. Based on the analysis of the limitations of traditional threshold method, the particle swarm optimization algorithm is used to select the optimal threshold of wavelet. Compared with the commonly hard and soft threshold method, the experiment results show that the method used in this paper is relatively stable, and can reduce noise excellently. The method can provide more accurate signal for quality analysis in laser cutting .So the method can be used in noise denoising of pulse-induced acoustic sound.

scholarly journals A Method Combining Fractal Analysis and Single Channel ICA for Vibration Noise Reduction

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Quanbo Lu ◽  
Mei Li
Keyword(s):  
Noise Reduction ◽  
Fractal Analysis ◽  
Single Channel ◽  
Vibration Signal ◽  
Wavelet Denoising ◽  
Vibration Signals ◽  
Time Frequency ◽  
Engineering Vibration ◽  
Ica Algorithm ◽  
Vibration Noise

Aiming at the problem that real engineering vibration signals are interfered by strong noise, this paper proposes a method combining single channel-independent component analysis (SCICA) and fractal analysis (FD) to reduce the effect of noise on the time-frequency analysis of vibration signals. First, phase space reconstruction is performed on the vibration signal to make the proper input for ICA algorithm. The original is then decomposed into several component signals. The fractal dimension of each component signals is calculated to determine whether the signal should be considered noise. Noisy component signals are then processed by wavelet denoising. Finally, the output signal after noise reduction is reconstructed using the filtered “right” component signals. This paper uses the method to analyze real noisy vibration signal. Experimental results show the effectiveness of the proposed method.

Research on Denoising of GPS Data Based on Nonlinear Wavelet Transform Threshold Method

2012 ◽  
Vol 446-449 ◽  
pp. 926-936
Author(s):  
De Bao Yuan ◽  
Xi Min Cui ◽  
Guo Wang ◽  
Jing Jing Jin ◽  
Wan Yang Xu
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Wavelet Denoising ◽  
Systematic Research ◽  
Gps Data ◽  
Signal Denoising ◽  
Threshold Method ◽  
Denoising Method

Signal denoising is one of the classic problems in the field of signal processing. As a new kind of signal processing tool, the good denoising performance of wavelet analysis has caused public growing concern and attention. The paper does systematic research on nonlinear wavelet threshold denoising method. And the wavelet denoising method has been used on GPS signal, and good results have been achieved.

Research of Image Denoising Method about Wavelet Transform with Neighborhood Average

2014 ◽  
Vol 989-994 ◽  
pp. 4054-4057 ◽  
Author(s):  
Chen Huang
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Wavelet Denoising ◽  
Good Time ◽  
Threshold Selection ◽  
Threshold Method ◽  
Denoising Method ◽  
Time Frequency ◽  
Image Details ◽  
Denoised Image

Because wavelet transform has good time-frequency characteristics, and its application in image denoising has been promising. Firstly, use the threshold method of the wavelet transform is used in removing image noise, and then the denoised image is smoothed using neighborhood average filtering with Gauss template. And wavelet denoising process and domain threshold selection principle are discussed. Simulation results show that this method can effectively reduce the noise and can remain most of image details better.

Improved Wavelet Threshold Denoising Method

2014 ◽  
Vol 602-605 ◽  
pp. 3177-3180
Author(s):  
Wei Ping Cui ◽  
Li Juan Du
Keyword(s):  
Traditional Method ◽  
Wavelet Denoising ◽  
Threshold Function ◽  
Signal Denoising ◽  
Improved Method ◽  
Denoising Method ◽  
Comparison And Analysis ◽  
Selection Of

In this paper, through comparison and analysis of various wavelet denoising methods, a new threshold function is constructed, and the selection of threshold is improved. Signal denoising simulation is made by the software MATLAB, the results show that the improved method is superior to the traditional method, and obtain a better denoising effect.

Adaptive Multiple Second-order Synchrosqueezing Wavelet Transform and Its Application in Wind Turbine Gearbox Fault Diagnosis

2021 ◽  
Author(s):  
Zhaohong Yu ◽  
Cancan Yi ◽  
Xiangjun Chen ◽  
Tao Huang
Keyword(s):  
Wavelet Transform ◽  
Wind Turbine ◽  
Time Window ◽  
Vibration Signal ◽  
Second Order ◽  
Center Frequency ◽  
Wind Turbine Gearbox ◽  
Adaptive Wavelet ◽  
Time Frequency ◽  
Window Width

Abstract Wind turbines usually operate in harsh environments and in working conditions of variable speed, which easily causes their key components such as gearboxes to fail. The gearbox vibration signal of a wind turbine has nonstationary characteristics, and the existing Time-Frequency (TF) Analysis (TFA) methods have some problems such as insufficient concentration of TF energy. In order to obtain a more apparent and more congregated Time-Frequency Representation (TFR), this paper proposes a new TFA method, namely Adaptive Multiple Second-order Synchrosqueezing Wavelet Transform (AMWSST2). Firstly, a short-time window is innovatively introduced on the foundation of classical Continuous Wavelet Transform (CWT), and the window width is adaptively optimized by using the center frequency and scale factor. After that, a smoothing process is carried out between different segments to eliminate the discontinuity and thus Adaptive Wavelet Transform (AWT) is generated. Then, on the basis of the theoretical framework of Synchrosqueezing Transform (SST) and accurate Instantaneous Frequency (IF) estimation by the utilization of second-order local demodulation operator, Adaptive Second-order Synchrosqueezing Wavelet Transform (AWSST2) is formed. Considering that the quality of actual time-frequency analysis is greatly disturbed by noise components, through performing multiple Synchrosqueezing operations, the congregation of TFR energy is further improved, and finally, the AMWSST2 algorithm studied in this paper is proposed. Since Synchrosqueezing operations are performed only in the frequency direction, this method AMWSST2 allows the signal to be perfectly reconstructed. For the verification of its effectiveness, this paper applies it to the processing of the vibration signal of the gearbox of a 750 kW wind turbine.

Wavelet decomposition and nonlinear prediction of nonstationary vibration signals

2020 ◽  
Vol 51 (3) ◽  
pp. 52-59 ◽  
Author(s):  
Xiao-bin Fan ◽  
Bin Zhao ◽  
Bing-xu Fan
Keyword(s):  
Wavelet Decomposition ◽  
Wavelet Packet ◽  
Prediction Method ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Maximum Lyapunov Exponent ◽  
Nonlinear Prediction ◽  
Local Method ◽  
Time Frequency ◽  
Nonstationary Vibration

In order to overcome the shortcomings (such as the time–frequency localization and the nonstationary signal analysis ability) of the Fourier transform, time–frequency analysis has been carried out by wavelet packet decomposition and reconstruction according to the actual nonstationary vibration signal from a large equipment located in a large Steel Corporation in this article. The effect of wavelet decomposition on signal denoising and the selection of high-frequency weight coefficients for each layer on signal denoising were analyzed. The nonlinear prediction of the chaotic time series was made by global method, local method, weighted first-order local method, and maximum Lyapunov exponent prediction method correspondingly. It was found the multi-step prediction method is better than other prediction methods.

scholarly journals Signal Denoising Method Based on Improved Wavelet Threshold Function for Microchip Electrophoresis C4D Equipment

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yaonan Tong ◽  
Jingui Li ◽  
Yaohui Xu ◽  
Lichen Cao
Keyword(s):  
Microchip Electrophoresis ◽  
Threshold Function ◽  
Signal Denoising ◽  
Threshold Method ◽  
Decomposition Level ◽  
Denoising Method ◽  
Capacitively Coupled ◽  
Signal Peak ◽  
Soft Threshold ◽  
Simulation Signal

A signal denoising method using improved wavelet threshold function is presented for microchip electrophoresis based on capacitively coupled contactless conductivity detection (ME-C4D) device. The evaluation results of denoising effect for the ME-C4D simulation signal show that using Daubechies 5 (db5) wavelet at a decomposition level 4 can produce the best performance. Furthermore, the denoising effect is compared with, as well as proved to be superior to, the existing techniques, such as Savitzky–Golay, Fast Fourier Transform, and soft threshold method. This method has been successfully applied to the self-developed ME-C4D equipment. After executing this method, the noise is cleanly removed, and the signal peak shape and peak area are well maintained.

ECG Signal Denoising Algorithm Based on Optimization Wavelet Transform

2013 ◽  
Vol 706-708 ◽  
pp. 785-788
Author(s):  
Guo Shun Yuan ◽  
Li Qing Geng
Keyword(s):  
Wavelet Transform ◽  
Median Filter ◽  
Noise Removal ◽  
Signal Denoising ◽  
Time Frequency ◽  
Multi Resolution Analysis ◽  
Signal Characteristics ◽  
Eigen Values ◽  
Resolution Analysis ◽  
Ecg Noise Removal

Wavelet transform algorithm with its unique multi-resolution analysis and it is in the time - frequency domain has the advantage of the ability to characterize the local signal characteristics, let it has been widely used in signal detection, noise removal, feature extraction, image compression and so on. In this paper, on the basis of already wavelet transform ECG noise removal, proposed a median filter optimization algorithm, enables ECG noise removal effect is more obvious, also for the Eigen values detection of ECG lay a better foundation.

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