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.

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.

The Research of the Spectral Features of Vibration Signals from Underground Railway Based on Wavelet Transform

2011 ◽  
Vol 243-249 ◽  
pp. 3463-3467
Author(s):  
Zhi Gang Yin
Keyword(s):  
Wavelet Transform ◽  
Signal Analysis ◽  
Spectral Features ◽  
Maximum Acceleration ◽  
Vibration Signals ◽  
Matlab Program ◽  
Underground Railway ◽  
Non Stationary Signal ◽  
Frequency Properties ◽  
Acceleration Signals

Using wavelet transform, signal’s frequency properties of vibration induced by underground are analyzed A MATLAB program is developed in order to decompose and reconstruct acceleration signals. The law that acceleration signals change in time-spectral domain is got. Then the relations of vibration signal’s maximum acceleration, energy, frequency and spectral are discussed. In contrast to conventional Fourier Transform， wavelet analysis can provide the evolution of spectral features of a signal as this evolves in time. It is ideal for random and non-stationary signal analysis.

Health Monitoring of a Gear Box Using Vibration Signal Analysis

2015 ◽  
Vol 813-814 ◽  
pp. 1012-1017 ◽  
Author(s):  
M.R. Praveen ◽  
M. Saimurugan
Keyword(s):  
Frequency Domain ◽  
Health Monitoring ◽  
Crucial Role ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Nearest Neighbours ◽  
Gear Box ◽  
Vibration Signal Analysis

A gear plays a crucial role in the performance of a gear box. The faults in a gear reduces the gear life and if problem arises in shaft it affects bearing. Gear box is finally affected due to these faults. Vibration signals carries information about condition of a gear box which are captured using piezoelectric accelerometer. In this paper, features are extracted and classified using K nearest neighbours (KNN) algorithms for both time and frequency domain. The effectiveness of KNN in classification of gear faults for both time and frequency domain is discussed and compared.

scholarly journals Vibration Condition Monitoring: Latest Trend and Review

2015 ◽  
Vol 773-774 ◽  
pp. 139-143
Author(s):  
K.H. Hui ◽  
L.M. Hee ◽  
M. Salman Leong ◽  
Ahmed M. Abdelrhman
Keyword(s):  
Decision Making ◽  
Fourier Transform ◽  
Condition Monitoring ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Machine Learning Techniques ◽  
Vibration Condition ◽  
The Past ◽  
Analysis Methods ◽  
Vibration Signal Analysis

Vibration analysis has proven to be the most effective method for machine condition monitoring to date. Various effective signal analysis methods to analyze and extract fault signature that embedded in the raw vibration signals have been introduced in the past few decades such as fast Fourier transform (FFT), short time Fourier transform (STFT), wavelets analysis, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), etc. however, these is still a need for human to interpret vibration signature of faults and it is regarded as one of the major challenge in vibration condition monitoring. Thus, most recent researches in vibration condition monitoring revolved around using Artificial Intelligence (AI) techniques to automate machinery faults detection and diagnosis. The most recent literatures in this area show that researches are mainly focus on using machine learning techniques for data fusion, features fusion, and also decisions fusion in order to achieve a higher accuracy of decision making in vibration condition monitoring. This paper provides a review on the most recent development in vibration signal analysis methods as well as the AI techniques used for automated decision making in vibration condition monitoring in the past two years.

scholarly journals The assessment of valve clearance in internal combustion engines based on parameters of vibration – methodological assumption

2004 ◽  
Vol 118 (1) ◽  
pp. 51-59
Author(s):  
Bartosz CZECHYRA ◽  
Grzegorz SZYMAŃSKI ◽  
Franciszek TOMASZEWSKI
Keyword(s):  
Signal Analysis ◽  
Internal Combustion Engines ◽  
Vibration Signal ◽  
Internal Combustion ◽  
Combustion Engines ◽  
Diagnostic Problem ◽  
Vibration Signals

In this article authors show the possibillities of using the parameters of vibration signals to estimate valve clearance in internal combustion engines. The main methodological assumptions of signal analysis and their results have been presented herein. The concept of research so as to solve the valve clearance diagnostic problem, based on the vibration signal, has been shown as well.

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