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.

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.

Analysis of Vibration Signals in Monitoring Titanium End Milling Process Using Triaxial Accelerometer

2021 ◽  
Author(s):  
John Henry Navarro-Devia ◽  
Dzung Viet Dao ◽  
Yun Chen ◽  
Huaizhong Li
Keyword(s):  
Titanium Alloys ◽  
Frequency Domain ◽  
Single Channel ◽  
End Milling ◽  
Cutting Parameters ◽  
Frequency Component ◽  
Spindle Speed ◽  
Triaxial Accelerometer ◽  
Vibration Signals ◽  
Time Frequency

Abstract Vibrations during milling of hard-to-cut materials can cause low productivity, inferior quality and short tool life. It is one of the common issues in the machining of hard-to-cut materials employed in aerospace applications, such as titanium alloys. This paper presents an analysis of the vibration signals in the 3 axes of movement during titanium end milling, under diverse cutting parameters, manipulating spindle speed and feed rate. Signals were obtained using a triaxial accelerometer and processed in MATLAB. The analysis was conducted in the frequency-domain and the time-frequency domain. The results show that high-frequency vibration could occur in any direction with different amplitudes. Response on each axis depends on spindle speed, feed, and type of milling. A frequency component continually appeared in each axis regardless of cutting conditions and is located near the natural frequencies. Finally, the triaxial accelerations were compared for the milling cases with a new and a worn tool. Results highlight the importance and need for continuous monitoring of vibration in the 3 axes, instead of only using a single-channel signal, providing experimental data which could expand knowledge relating to the milling of titanium alloys.

scholarly journals Application of Choi—Williams Reduced Interference Time Frequency Distribution to Machinery Diagnostics

1995 ◽  
Vol 2 (6) ◽  
pp. 437-444 ◽  
Author(s):  
Howard A. Gaberson
Keyword(s):  
Fourier Transform ◽  
Vibration Signal ◽  
Test Cases ◽  
Short Time Fourier Transform ◽  
Vibration Signals ◽  
Time Frequency ◽  
Machinery Diagnostics ◽  
Short Time ◽  
Amplitude And Frequency Modulation ◽  
Time Frequency Distribution

This article discusses time frequency analysis of machinery diagnostic vibration signals. The short time Fourier transform, the Wigner, and the Choi–Williams distributions are explained and illustrated with test cases. Examples of Choi—Williams analyses of machinery vibration signals are presented. The analyses detect discontinuities in the signals and their timing, amplitude and frequency modulation, and the presence of different components in a vibration signal.

scholarly journals Nonnegative Matrix Factorization of time frequency representation of vibration signal for local damage detection – comparison of algorithms

2018 ◽  
Vol 29 ◽  
pp. 00010
Author(s):  
Jacek Wodecki
Keyword(s):  
Damage Detection ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Single Channel ◽  
Classical Method ◽  
Nonnegative Matrix ◽  
Vibration Signal ◽  
Local Damage ◽  
Time Frequency ◽  
Frequency Representation

Local damage detection in rotating machine elements is very important problem widely researched in the literature. One of the most common approaches is the vibration signal analysis. Since time domain processing is often insufficient, other representations are frequently favored. One of the most common one is time-frequency representation hence authors propose to separate internal processes occurring in the vibration signal by spectrogram matrix factorization. In order to achieve this, it is proposed to use the approach of Nonnegative Matrix Factorization (NMF). In this paper three NMF algorithms are tested using real and simulated data describing single-channel vibration signal acquired on damaged rolling bearing operating in drive pulley in belt conveyor driving station. Results are compared with filtration using Spectral Kurtosis, which is currently recognized as classical method for impulsive information extraction, to verify the validity of presented methodology.

Gearbox Systems Dynamic Modelling for Diagnostic Fault Detection

2003 ◽  
Author(s):  
Walter Bartelmus ◽  
Radosław Zimroz
Keyword(s):  
Computer Simulation ◽  
Mathematical Modelling ◽  
Dynamic Modelling ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Time Frequency ◽  
Network Training ◽  
Condition Change ◽  
Tooth Stiffness ◽  
Distributed Faults

The paper deals with mathematical modelling and computer simulation of a gearbox driving system with a double stage gearbox. Mathematical modelling and computer simulations are used for supporting diagnostic inference. Vibration is thought of as a signal of gear condition. It is stressed that vibration generated by gears is influenced by many factors. These factors are divided into four groups: design, production technology, operational, condition change. The condition change of a gearbox is given by gear faults that are divided into single faults such as a tooth crack or breakage or distributed faults as pitting, scuffing, and erosion. The faults are modelled in the case of a crack as a change of tooth stiffness in the case of distributed faults they are given multi-parameter functions. Simulated signals undergo signal analysis by spectrum, cepstrum, time-frequency spectrogram. It has been shown by computer simulation that single and distributed faults are identified by cepstrum. For explicit fault identification time-frequency spectrogram has to be additionally used. The computer simulation results are confirmed by analysis of measured vibration signals received from a gearbox wall/housing. The aim of mathematical modelling and computer simulation, besides finding the relationship between gear condition and vibration signal is in the future to give vibration signals for neural network training.

Blasting Vibration Signals Time-Frequency Analysis Based on S-Transform

2012 ◽  
Vol 588-589 ◽  
pp. 2013-2017
Author(s):  
Dong Tao Li ◽  
Jing Long Yan ◽  
Le Zhang
Keyword(s):  
Frequency Analysis ◽  
Vibration Signal ◽  
Transformation Method ◽  
Blasting Vibration ◽  
Time Duration ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Vibration Wave

Introduced the theory of S-transform, designed simulation experiment and the frequency components distribution versus time was, verified that the S-transformation method is suitable for blasting vibration signal time-frequency analyzed. Applied it to the time-frequency analysis of measured blasting vibration signals at situ, the results show that S-transform has excellent time-frequency representation ability and higher resolution, reveals the detail information of blasting vibration wave changing with time and frequency, and provides a new way for blasting vibration research. Determined the desired delay intervals through comparing the energy of signal and the time duration of the waveform at characteristic frequency between two-hole blasting vibration signals with different delay intervals.

scholarly journals Multistage noise reduction processing for vibration signal of hydropower units

2021 ◽  
Vol 2108 (1) ◽  
pp. 012008
Author(s):  
Yousong Shi ◽  
Jianzhong Zhou
Keyword(s):  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Field Testing ◽  
Signal To Noise ◽  
Vibration Signals ◽  
Multi Stage ◽  
Actual Field ◽  
Stationary Vibration ◽  
Testing Environments

Abstract In actual field testing environments of hydropower units, unit vibration signals are often contaminated with noise. In order to obtain the real vibration signal, a multi-stage vibration signal denoise method SG-SVD-VMD is proposed for the guide bearing nonlinear and non-stationary vibration signals. And the root mean square error (RMSE) and signal to noise ratio (SNR) are used to evaluate the noise reduction ability of eight methods. The results show that the noise-canceling ability of this proposed method has improved to some extent. It can effectively suppress the noise of the hydropower units vibration signals. This method can effectively identify the shaft track and the running state of hydropower units.

The Wavelet De-Noising of Vibration Signals for Aircraft Rolling Bearings

2014 ◽  
Vol 912-914 ◽  
pp. 873-877 ◽  
Author(s):  
Feng Kui Cui ◽  
Fei Fei Lv ◽  
Xiao Qiang Wang ◽  
Dong Ying Zhang
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Noise Reduction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Noise Interference ◽  
Low Snr ◽  
Contrast Analysis

Aiming at air rolling bearing vibration signals low SNR and nonstationary characteristics, taking wavelet theory and principles of the wavelet noise reduction for air vibration signals of rolling bearings to conduct wavelet noise reduction processing.By means of the simulation signal wavelet noise reduction processing and fast Fourier transform, the contrast analysis of the vibration signals after wavelet noise reduction and FFT transform and the original signal directly to the result of the fast Fourier transform, and thus prove the validity of the vibration signal wavelet noise reduction. Through the actual vibration signals of bearing conductnoise reduction processing, the result is a further indication of the superiority of wavelet noise reduction in eliminate noise interference.

Time-Frequency Characteristics of Vibration Signals Analysis for Large Instantaneous Impact Mill Rolling Based on Wavelet Packet

2013 ◽  
Vol 834-836 ◽  
pp. 1061-1064
Author(s):  
Qi Jun Xiao ◽  
Zhong Hui Luo
Keyword(s):  
Time Domain ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Signal Wave ◽  
Reconstruction Technique ◽  
Wavelet Packet Decomposition ◽  
Vibration Signals ◽  
Time Frequency ◽  
Impact Vibration ◽  
Signal Time

The wavelet packet decomposition and reconstruction technique is applied to time-frequency analysis of bite steel impact vibration signal by big rolling machine, it is obtained the bite steel impact signal wave packet. According to the size of the wavelet packet energy, it is reconstructed the signal of No.1 and No.2 wavelet packet. According to reconstruction of the signal time domain waveform and FFT spectrum chart, some meaningful conclusions are obtained.

Mixed-Mode Signal Detection of Road Vehicle Vibration Using Hilbert-Huang Transform

2015 ◽  
Author(s):  
Julien Lepine ◽  
Michael Sek ◽  
Vincent Rouillard
Keyword(s):  
Mixed Mode ◽  
Vibration Signal ◽  
Significance Test ◽  
Mathematical Framework ◽  
Vehicle Speed ◽  
Road Vehicle ◽  
Vibration Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Vibration

The Hilbert-Huang Transform (HHT) is a fully adaptive time-frequency analysis method which is applicable to nonlinear and nonstationary processes. However, this promising method is fairly new and its range of applications is not well known. Furthermore, its mathematical framework is not yet fully developed. So far, the HHT has yielded interesting results for many applications such as biomedical, geophysical, meteorological and health monitoring, but there is no evidence of its application on complex mixed-mode vibration signals. To fill that gap, this paper investigates the application of the HHT to detect the different modes of road vehicle vibration signals. These modes originate from road roughness variation and vehicle speed which create nonstationary random vibration. Other modes are due to road surface aberrations which create transient events and the engine and drive train system of the vehicle which create harmonic vibrations. The energy density/average period significance test based on the HHT is assessed to detect these modes. The results, based on purposefully created synthetic test signals, reveal the limitations and shortcomings of the HHT based technique to detect and separate the various components of the mixed-mode vibration signals such as vehicle vibration signal.

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