Study on Noise Elimination of Mechanical Vibration Signal Based on Improved Wavelet

2020 ◽  
Author(s):  
Wei Yuan
Keyword(s):  
Mechanical Vibration ◽  
Vibration Signal ◽  
Noise Elimination

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.

Research of Ni-Mn-Ga Shape Memory Alloy Based Mechanical Vibration Detection Device

2014 ◽  
Vol 1006-1007 ◽  
pp. 845-848
Author(s):  
Yong Zhi Cai
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Experimental Results ◽  
Vibration Measurements ◽  
Vibration Detection ◽  
Vibration Sensing

The study explores the vibration sensing effect of Ni-Mn-Ga shape memory alloy, based on the experimental results, researched the characteristics of this alloy applied in mechanical vibration signal sensors, and describes the feasibility of this alloy used for vibration measurements.

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.

Detecting Damage of Rolling Bearings Using EMD

2005 ◽  
Vol 293-294 ◽  
pp. 753-760 ◽  
Author(s):  
Qiang Gao ◽  
Zheng Jia He ◽  
Xue Feng Chen ◽  
Ke Yu Qi
Keyword(s):  
Mechanical Vibration ◽  
Vibration Signal ◽  
Analysis Method ◽  
Rolling Bearings ◽  
High Frequency Signal ◽  
Outer Race ◽  
Mode Decomposition ◽  
Envelope Signal ◽  
Inner Race ◽  
Emd Method

Empirical mode decomposition (EMD) method is introduced, and a new EMD based approach for damage detection of rolling bearings is presented. In this approach, the characteristic high-frequency signal with amplitude modulation of rolling bearings with local damage is separated from the mechanical vibration signal as an intrinsic mode function (IMF) by using EMD, and an envelope signal can be obtained by using Hilbert transform. Then, the characteristic frequency of damage of rolling bearings is extracted by applying Fourier transform to the envelope signal. The presented approach is used to analyse experimental signals collected from rolling bearings with outer race damage or inner race damage, and the results indicate that the EMD based approach can detect damage of rolling bearings more effectively comparing with traditional envelope analysis method.

Sign in / Sign up

Export Citation Format

Share Document