Application of an Improved Wavelet Threshold Denoising Method for Vibration Signal Processing

2014 ◽  
Vol 889-890 ◽  
pp. 799-806 ◽  
Author(s):  
Zhi Jie Xie ◽  
Bao Yu Song ◽  
Yang Zhang ◽  
Feng Zhang
Keyword(s):  
Signal Processing ◽  
Wavelet Decomposition ◽  
Wavelet Coefficient ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Threshold Function ◽  
Denoising Method ◽  
Threshold Functions ◽  
Vibration Signal Analysis ◽  
White Noises

Vibration signal analysis has been widely used in the fault detection and condition monitoring of rotation machinery. But the practical signals are easily polluted by noises in their transmission process. The raw signals should be processed to reduce noise and improve the quality before further analyzing. In this paper an improved wavelet threshold denosing method for vibration signal processing is proposed. Firstly, a new threshold is developed based on the VisuShrink threshold. The effect of noise standard deviation and wavelet coefficient is retained, and the correlation of wavelet decomposition scale is considered. Then, a new threshold function is defined. The new algorithm is able to overcome the discontinuity in hard threshold denoising method and reduce the distortion caused by permanent bias of wavelet coefficient in soft threshold denoising method. At last five kinds of threshold principles and three kinds of threshold functions are compared in processing the same signal, which is simulated as the mechanical vibration signal added white noises. The results show that the improved threshold is superior to the traditional threshold principles and the new threshold function is more effective than soft and hard threshold function in improving SNR and decreasing RMSE.

scholarly journals Mechanical Vibration Signal Denoising Using Quantum-Inspired Standard Deviation Based on Subband Based Gaussian Mixture Model

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Aidong Xu ◽  
Wenqi Huang ◽  
Peng Li ◽  
Huajun Chen ◽  
Jiaxiao Meng ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Gaussian Mixture ◽  
Wavelet Coefficients ◽  
Denoising Method ◽  
Reduction Effect ◽  
Thresholding Function

Aiming at improving noise reduction effect for mechanical vibration signal, a Gaussian mixture model (SGMM) and a quantum-inspired standard deviation (QSD) are proposed and applied to the denoising method using the thresholding function in wavelet domain. Firstly, the SGMM is presented and utilized as a local distribution to approximate the wavelet coefficients distribution in each subband. Then, within Bayesian framework, the maximum a posteriori (MAP) estimator is employed to derive a thresholding function with conventional standard deviation (CSD) which is calculated by the expectation-maximization (EM) algorithm. However, the CSD has a disadvantage of ignoring the interscale dependency between wavelet coefficients. Considering this limit for the CSD, the quantum theory is adopted to analyze the interscale dependency between coefficients in adjacent subbands, and the QSD for noise-free wavelet coefficients is presented based on quantum mechanics. Next, the QSD is constituted for the CSD in the thresholding function to shrink noisy coefficients. Finally, an application in the mechanical vibration signal processing is used to illustrate the denoising technique. The experimental study shows the SGMM can model the distribution of wavelet coefficients accurately and QSD can depict interscale dependency of wavelet coefficients of true signal quite successfully. Therefore, the denoising method utilizing the SGMM and QSD performs better than others.

scholarly journals Vibration Signal Analysis of Journal Bearing Supported Rotor System by Cyclostationarity

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Chang Peng ◽  
Lin Bo
Keyword(s):  
Journal Bearing ◽  
Wigner Distribution ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Processing Technique ◽  
Rotor System ◽  
Signal Processing Technique ◽  
Spectral Correlation ◽  
Useful Signal ◽  
Vibration Signal Analysis

Cyclostationarity has been widely used as a useful signal processing technique to extract the hidden periodicity of the energy flow of the mechanical vibration signature. However, the conventional cyclostationarity is restricted to analyzing the real-valued signal, which is incapable of processing the constructed complex-valued signal obtained from the journal bearing supported rotor system operating with oil film instability. In this work, the directional cyclostationary parameters, such as directional cyclic mean, directional cyclic autocorrelation, and directional spectral correlation density, are defined based on the principle of directional Wigner distribution. Practical experiment has demonstrated the effectiveness and superiority of the proposed method in the investigation of the instantaneous planar motion of the journal bearing supported rotor system.

Shaft Vibration Signal Denoising Based on Wavelet Analysis

2013 ◽  
Vol 300-301 ◽  
pp. 1110-1113
Author(s):  
Tie Qiang Sun ◽  
Rong Liu ◽  
Zhi Qi Qiu
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Wavelet Packet ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Axial Vibration ◽  
Denoising Method ◽  
Original Source ◽  
Separation Result

In actual , there exist inevitably a lot of interference from neighbor machine and noise from surrondings in mechanical vibration signal measured by sensor ,which is disadvantageous for condition monitoring and fault diagnosis. In order to eliminate the axial vibration signal in the noise, using Wavelet packet denoising method in this article, Emulating experiment s were carried out under the MATLAB software ,original signals adopted vibration impulsion signal produced by vice position of faulty bear. Separation result s confirm this method successfully ext ract original source ,efficiently removes noise.

MATHEMATICAL IMPLEMENTATION OF HYBRID FAST FOURIER TRANSFORM AND DISCRETE WAVELET TRANSFORM FOR DEVELOPING GRAPHICAL USER INTERFACE USING VISUAL BASIC FOR SIGNAL PROCESSING APPLICATIONS

2012 ◽  
Vol 12 (05) ◽  
pp. 1240031 ◽  
Author(s):  
MOUSA K. WALI ◽  
M. MURUGAPPAN ◽  
R. BADLISHAH AHMMAD
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Input Signal ◽  
Wavelet Decomposition ◽  
Wavelet Coefficient ◽  
Visual Basic ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
End User ◽  
Decomposition Level

In recent years, the application of discrete wavelet transform (DWT) on biosignal processing has made a significant impact on developing several applications. However, the existing user-friendly software based on graphical user interfaces (GUI) does not allow the freedom of saving the wavelet coefficients in .txt or .xls format and to analyze the frequency spectrum of wavelet coefficients at any desired wavelet decomposition level. This work describes the development of mathematical models for the implementation of DWT in a GUI environment. This proposed software based on GUI is developed under the visual basic (VB) platform. As a preliminary tool, the end user can perform "j" level of decomposition on a given input signal using the three most popular wavelet functions — Daubechies, Symlet, and Coiflet over "n" order. The end user can save the output of wavelet coefficients either in .txt or .xls file format for any further investigations. In addition, the users can gain insight into the most dominating frequency component of any given wavelet decomposition level through fast Fourier transform (FFT). This feature is highly essential in signal processing applications for the in-depth analysis on input signal components. Hence, this GUI has the hybrid features of FFT with DWT to derive the frequency spectrum of any level of wavelet coefficient. The novel feature of this software becomes more evident for any signal processing application. The proposed software is tested with three physiological signal — electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG) — samples. Two statistical features such as mean and energy of wavelet coefficient are used as a performance measure for validating the proposed software over conventional software. The results of proposed software is compared and analyzed with MATLAB wavelet toolbox for performance verification. As a result, the proposed software gives the same results as the conventional toolbox and allows more freedom to the end user to investigate the input signal.

BOU-Based Cycle Determination for Different Kinds of Mechanical Vibration Signals

2012 ◽  
Vol 220-223 ◽  
pp. 2217-2223 ◽  
Author(s):  
Miao Rong Lv ◽  
Jian Lu ◽  
Zhi Qiang Chen
Keyword(s):  
Signal Processing ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Simulation Method ◽  
Fault Detection And Diagnosis ◽  
Basic Operation ◽  
Vibration Signals ◽  
Operation Unit ◽  
Simulation Results ◽  
Detection And Diagnosis

The cycle of each vibration component of a mixed signal is a very important parameter for reciprocating mechanical vibration signal processing. The cycle determination also has an important influence to obtain the typical characteristics of every sub-signal, to achieve the fault detection and diagnosis of the equipment. But signals acquired at the scene tend to a mixture of a variety of vibration components that have their own periodic characteristics. This paper mainly proposes a method based on the conception of the basic operation unit(BOU) for mechanical vibration signal processing, and the principles and processes of this method are described in detail. Simulation method is introduced in order to explain this principles. Furthermore, A Delphi programming is developed to realize this simulation implementation, and simulation results are demonstrated to fully verify its correctness. Finally, a method to quickly determine the various vibration component cycles is put forward.

scholarly journals Vibration Signal Analysis of Water Seal Blasting Based on Wavelet Threshold Denoising and HHT Transformation

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jiang-chao Liu ◽  
Wen-xue Gao
Keyword(s):  
Vibration Signal ◽  
Frequency Noise ◽  
Blasting Vibration ◽  
Threshold Method ◽  
Denoising Method ◽  
Low Frequencies ◽  
Vibration Signals ◽  
Signal Energy ◽  
Vibration Signal Analysis ◽  
Water Seal

The blasting vibration signal obtained from tunnel construction monitoring is affected by the external environment, which contains a lot of noise that causes distortion during signal processing. To analyse the blasting vibration signal and determine the appropriate water seal blasting charge structure for construction, combined with wavelet threshold denoising method and HHT transformation, the blasting vibration signals of the four charge structures of conventional charge, water interval charge at both ends, water interval charge at the orifice, and water interval charge at the hole bottom are denoised and HHT is analysed. The results show that the wavelet threshold method can effectively eliminate high-frequency noise in the blasting vibration signals and retain information carried by the vibration signal itself. The frequency and energy of the blasting vibration signals of the water interval charge at both ends are densely distributed in the range of 0 s to 0.9 s and below 100 Hz. The frequency and energy of the blasting signals of the other three charging structures are reduced within the same range, sparse areas appear, and the instantaneous total energy is smaller than that with a water interval charge at both ends, which shows that the water interval charge at both ends can effectively apply explosive energy to the surrounding rock and reduce energy loss in the explosive. The blasting vibration signal energy of the water interval charge at both ends is mainly concentrated in components IMF2 to IMF5, and the corresponding frequencies are concentrated at 6 Hz to 11 Hz and 20 Hz to 70 Hz, while the blasting vibration signal energy of other three charge structures is mainly distributed in components IMF2 to IMF4, corresponding frequencies are concentrated within 20 Hz to 70 Hz, and the distribution at low frequencies is not obvious. Therefore, when using the water interval charge at both ends, it is necessary to increase the main vibration frequency of the original vibration signals by reducing the single section charge and using frequency shift technology to avoid the natural frequency of the structure and reduce resonance-induced damage.

Oscillatory behavior-based wavelet decomposition for the monitoring of bearing condition in centrifugal pumps

2017 ◽  
Vol 232 (6) ◽  
pp. 757-772 ◽  
Author(s):  
Anil Kumar ◽  
Rajesh Kumar
Keyword(s):  
Signal Processing ◽  
Wavelet Decomposition ◽  
Simulation Analysis ◽  
Vibration Signal ◽  
Frequency Factor ◽  
Oscillatory Behavior ◽  
Q Factor ◽  
Centrifugal Pumps ◽  
Processing Scheme ◽  
Bearing Defect

Bearing failure is one of the reasons for centrifugal pump breakdown. Existing methods developed for bearing fault diagnosis do not work satisfactorily when the vibration signature of bearing is overlapped by the signature from other defect sources such as an impeller defect. A vibration signal processing scheme making use of ensemble empirical mode decomposition and dual Q-factor wavelet decomposition is proposed to extract information of the bearing defect in a pump. A criterion called as frequency factor is also proposed to find the best decomposition level for the given high and low Q-factor wavelet decomposition parameters. The transient impulses due to bearing defect are effectively extracted separating traces of oscillatory signature of impeller defect and the noise in the signal. The same has been demonstrated using simulation analysis and experimental study. A comparison of the proposed method with existing signal processing methods is also presented.

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.

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