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.

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.

scholarly journals A New Method of Denoising of Vibration Signal and Its Application

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Feng Miao ◽  
Rongzhen Zhao ◽  
Xianli Wang
Keyword(s):  
White Noise ◽  
Wavelet Packet ◽  
Median Filter ◽  
Vibration Signal ◽  
New Method ◽  
Mixed Complex ◽  
Denoising Method ◽  
Adaptive Wavelet ◽  
Vibration Signals ◽  
Window Width

In order to improve the performance of the denoising method for vibration signals of rotating machinery, a new method of signal denoising based on the improved median filter and wavelet packet technology is proposed through analysing the characteristics of noise components and relevant denoising methods. Firstly, the window width of the median filter is calculated according to the sampling frequency so that the impulse noise and part of the white noise can be effectively filtered out. Secondly, an improved self-adaptive wavelet packet denoising technique is used to remove the residual white noise. Finally, useful vibration signals are obtained after the previous processing. Simulation signals and rotor experimental vibration signals were used to verify the performance of the method. Experiment results show that the method can not only effectively eliminate the mixed complex noises but also preserve the fault character details, which demonstrates that the proposed method outperforms the method based on the wavelet-domain median filter.

scholarly journals THE USE OF A WAVELET TRANSFORMATION FOR REMOVAL OF THE NOISE COMPONENT FROM THE VIBRO SIGNAL

2021 ◽  
pp. 106-111
Author(s):  
А. Передерко
Keyword(s):  
Wavelet Transform ◽  
Signal Analysis ◽  
Wavelet Transformation ◽  
Vibration Signal ◽  
Distribution Law ◽  
Noise Component ◽  
Vibration Signals ◽  
The Past ◽  
Principle Of Causality ◽  
Filtering Problem

The article investigates the use of wavelets to remove noise from the measuring vibration signal. It is determined that wavelets are well adapted for signal analysis, for which the principle of causality is important: wavelets preserve the direction of time and do not create parasitic interference between the past and the future. Criteria for selecting an analytical wavelet have been developed, depending on what information should be extracted from the signal and the need to more fully identify and emphasize certain properties of the analyzed signal. It is proposed to use Daubechies wavelets to process the vibration signal data. The simulation of vibration signal filtering from noise with the normal distribution law is performed in the MATCAD package. It is proved that the method of wavelet transform allows to solve the problem of filtering the vibration signal from noise when processing vibration signals obtained by autonomous recording devices in conditions of increased interference from the environment. The obtained results evidence to the prospects of the developed method and its advantages in comparison with the hardware solution of the filtering problem.

Derivation of a Damage Sensitive Feature Using the Haar Wavelet Transform

2009 ◽  
Vol 76 (6) ◽  
Author(s):  
K. Krishnan Nair ◽  
Anne S. Kiremidjian
Keyword(s):  
Wavelet Transform ◽  
Wavelet Decomposition ◽  
Vibration Signal ◽  
Haar Wavelet ◽  
Physical Parameters ◽  
Wavelet Coefficients ◽  
Vibration Signals ◽  
Haar Wavelet Transform ◽  
Loading Function ◽  
Feature Based

In this paper, a damage sensitive feature based on the wavelet transform of the vibration signal is derived. The theoretical aspects of wavelet decomposition of vibration signals are presented. It is shown that the energies of the wavelet coefficients at appropriate scales can be used as damage sensitive features. Expressions for the energies of wavelet coefficients using the Haar wavelet basis function are derived for a single degree and a multidegree of freedom system. It is shown that the energies of the wavelet coefficients extracted at higher scales are functions of the physical parameters of the system and the loading function. Finally, the migration of damage sensitive feature vectors with damage is illustrated for the ASCE Benchmark Structure.

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.

A method of eliminating the vibration signal noise of hydropower unit based on NA-MEMD and approximate entropy

2016 ◽  
Vol 231 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Xueli An ◽  
Junjie Yang
Keyword(s):  
Empirical Mode Decomposition ◽  
Real World ◽  
Approximate Entropy ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Simulation Signal

A new vibration signal denoising method of hydropower unit based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and approximate entropy is proposed. Firstly, the NA-MEMD is used to decompose the signal into a number of intrinsic mode functions. Then, the approximate entropy of each component is computed. According to a preset threshold of approximate entropy, these components are reconstructed to denoise vibration signal of hydropower unit. The analysis results of simulation signal and real-world signal show that the proposed method is adaptive and has a good denoising performance. It is very suitable for online denoising of hydropower unit's vibration signal.

Signal Denoising Method Based on KICA by Noise Components

2013 ◽  
Vol 329 ◽  
pp. 269-273
Author(s):  
Ling Li Jiang ◽  
Ping Li ◽  
Bo Zeng
Keyword(s):  
Independent Component Analysis ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Independent Component ◽  
Signal Denoising ◽  
Denoising Method ◽  
One Dimensional ◽  
Kernel Independent Component Analysis ◽  
Multidimensional Signal

Denoising is an essential part of fault signal analysis. This paper proposes a kernel independent component analysis (KICA)-based denoising method for removing the noise from vibration signal. By introducing noise components of the observed signal, one-dimensional observed signal is extended to multi-dimensional signal. Then performing KICA on multidimensional signal, the noise in the observed signal consistent with the introduced noise will be removed that achieve the purpose of denosing. The effectiveness of the proposed method is demonstrated by the case study.

Mechanical faulty signal denoising using a redundant non-linear second-generation wavelet transform

2011 ◽  
Vol 225 (4) ◽  
pp. 799-808 ◽  
Author(s):  
N Li ◽  
R Zhou ◽  
X Z Zhao
Keyword(s):  
Wavelet Transform ◽  
Second Generation ◽  
Selection Criterion ◽  
Signal Denoising ◽  
Mechanical Equipment ◽  
Denoising Method ◽  
Second Generation Wavelet Transform ◽  
Non Linear ◽  
Early Fault Detection ◽  
Second Generation Wavelet

Denoising and extraction of the weak signals are crucial to mechanical equipment fault diagnostics, especially for early fault detection, in which cases fault features are very weak and masked by the noise. The wavelet transform has been widely used in mechanical faulty signal denoising due to its extraordinary timefrequency representation capability. However, the mechanical faulty signals are often non-stationary, with the structure varying significantly within each scale. Because a single wavelet filter cannot mimic the signal structure of an entire scale, the traditional wavelet-based signal denoising method cannot achieve an ideal effect, and even worse some faulty information of the raw signal may be lost in the denoising process. To overcome this deficiency, a novel mechanical faulty signal denoising method using a redundant non-linear second generation wavelet transform is proposed. In this method, an optimal prediction operator is selected for each transforming sample according to the selection criterion of minimizing each individual prediction error. Consequently, the selected predictor can always fit the local characteristics of the signals. The signal denoising results from both simulated signals and experimental data are presented and both support the proposed method.

Wavelet Denoising Method for RF Signal

2014 ◽  
Vol 955-959 ◽  
pp. 911-915
Author(s):  
Shu Lai Liang ◽  
Zhou Guo Hou ◽  
Zhao Peng Li
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Virtual Instrument ◽  
Signal Denoising ◽  
Transform Method ◽  
Denoising Method ◽  
System A ◽  
Rfid System ◽  
Research On Application ◽  
Rf Signal

To reduce the noises in received RF signal and improve the performance of RFID system, a novelty denoising method of wavelet transform in RFID system was proposed in this paper. It analyzed wavelet transform method and FFT method,then introduced wavelet transform principle and wavelet coefficients threshold denoising method. According to the standard ISO18000-3,the RF signal at 13.56MHz was simulated using modulation toolbox in LabVIEW environment. At last, the RF signal with Gauss white noise together, was denoised by wavelet transform method and FFT method respectively. Simulation results showed that the wavelet transform denoising method has higher accuracy, better effect, compared with traditional FFT denoising method. The research on application of wavelet analysis in RF signal denoising is significant and practical. The wavelet analysis is applied to the field of virtual instrument, which can provide people with more accurate and convenient testing, and has good application prospects in engineering applications.

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