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.

scholarly journals Noise reduction and signal-to-noise ratio improvement of atomic magnetometers with optical gradiometer configurations

2015 ◽  
Vol 23 (5) ◽  
pp. 6976 ◽  
Author(s):  
Keigo Kamada ◽  
Yosuke Ito ◽  
Sunao Ichihara ◽  
Natsuhiko Mizutani ◽  
Tetsuo Kobayashi
Keyword(s):  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio

Local Geometric Projection-Based Noise Reduction for Vibration Signal Analysis in Rolling Bearings

2008 ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao ◽  
Kang B. Lee ◽  
Steven E. Fick
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Noise Reduction ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Multifractal Spectrum ◽  
Vibration Signal ◽  
Rolling Bearings ◽  
One Dimensional ◽  
Vibration Signal Analysis

This paper presents a noise reduction technique for vibration signal analysis in rolling bearings, based on local geometric projection (LGP). LGP is a non-linear filtering technique that reconstructs one dimensional time series in a high-dimensional phase space using time-delayed coordinates, based on the Takens embedding theorem. From the neighborhood of each point in the phase space, where a neighbor is defined as a local subspace of the whole phase space, the best subspace to which the point will be orthogonally projected is identified. Since the signal subspace is formed by the most significant eigen-directions of the neighborhood, while the less significant ones define the noise subspace, the noise can be reduced by converting the points onto the subspace spanned by those significant eigen-directions back to a new, one-dimensional time series. Improvement on signal-to-noise ratio enabled by LGP is first evaluated using a chaotic system and an analytically formulated synthetic signal. Then analysis of bearing vibration signals is carried out as a case study. The LGP-based technique is shown to be effective in reducing noise and enhancing extraction of weak, defect-related features, as manifested by the multifractal spectrum from the signal.

Detecting and Predicting Early Faults of Complex Rotating Machinery Based on Cyclostationary Time Series Model

2006 ◽  
Vol 128 (5) ◽  
pp. 666-671 ◽  
Author(s):  
Z. S. Chen ◽  
Y. M. Yang ◽  
Z. Hu ◽  
G. J. Shen
Keyword(s):  
Signal To Noise Ratio ◽  
Rotating Machinery ◽  
Ar Model ◽  
Model Parameters ◽  
Time Varying ◽  
Signal To Noise ◽  
Vibration Signals ◽  
Novel Method ◽  
Cyclostationary Signals ◽  
Gear Crack

Vibration signals of complex rotating machinery are often cyclostationary, so in this paper one novel method is proposed to detect and predict early faults based on the linear (almost) periodically time-varying autoregressive (LPTV-AR) model. At first the algorithms of identifying model parameters and order are presented using the higher-order cyclic-cumulant, which can suppress additive stationary noises and improve the signal to noise ratio (SNR). Then numerical simulations are done and the results indicate that this model is more effective for cyclostationary signals than the classical AR model. In the end the proposed method is used for detecting incipient gear crack fault in a helicopter gearbox. The results demonstrate that the approach can be used to detect and predict early faults of complex rotating machinery by the kurtosis of the residual signal.

Threshold Noise Reduction Research of Improved EEMD Method

2012 ◽  
Vol 226-228 ◽  
pp. 237-240 ◽  
Author(s):  
Mei Jun Zhang ◽  
Hao Chen ◽  
Chuang Wang ◽  
Qing Cao
Keyword(s):  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Frequency Noise ◽  
Signal To Noise ◽  
Time Frequency ◽  
Noise Ratio ◽  
Reduction Effect ◽  
Eemd Method ◽  
Simulation Signal ◽  
Improved Eemd

In order to extract effectively detection signals in the noise background for non-stationary signal.On the basis of EEMD, improved EEMD is put forward, the improve EEMD threshold noise reduction is researched in this paper.The simulation signal compared the noise reduction effect of the wavelet,EMD,EEMD,and the improved EEMD. The improved EEMD threshold noise reduction have the best noise reduction result , the highest signal-to-noise ratio, the smallest standard deviation error.After the improved EEMD threshold noise reduction , the measurement signal time domain waveform smooth. More high frequency noise was obviously reduced in Hilbert time- frequency spectrum. Signal-to-noise ratio significantly improve, and signal characteristics are very clear.

Mechanical Fault Detection Based on the Wavelet De-Noising Technique

2004 ◽  
Vol 126 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Jing Lin ◽  
Ming J. Zuo ◽  
Ken R. Fyfe
Keyword(s):  
Prior Information ◽  
Early Stage ◽  
Signal To Noise Ratio ◽  
Mechanical Vibration ◽  
Likelihood Estimation ◽  
Morlet Wavelet ◽  
Signal To Noise ◽  
Vibration Signals ◽  
Orthogonal Wavelets ◽  
Basic Wavelet

For gears and roller bearings, periodic impulses indicate that there are faults in the components. However, it is difficult to detect the impulses at the early stage of fault because they are rather weak and often immersed in heavy noise. Existing wavelet threshold de-noising methods do not work well because they use orthogonal wavelets, which do not match the impulse very well and do not utilize prior information on the impulse. A new method for wavelet threshold de-noising is proposed in this paper; it not only employs the Morlet wavelet as the basic wavelet for matching the impulse, but also uses the maximum likelihood estimation for thresholding by utilizing prior information on the probability density of the impulse. This method has performed excellently when used to de-noise mechanical vibration signals with a low signal-to-noise ratio.

scholarly journals Predicting COVID-19 Pandemic in Saudi Arabia Using Modified Singular Spectrum Analysis

2020 ◽  
Author(s):  
Nader Alharbi
Keyword(s):  
Saudi Arabia ◽  
Noise Reduction ◽  
Spectrum Analysis ◽  
Signal To Noise Ratio ◽  
Singular Spectrum Analysis ◽  
Real Data ◽  
Signal Extraction ◽  
Signal To Noise ◽  
Singular Spectrum ◽  
Data Points

Abstract This research presents a modified Singular Spectrum Analysis (SSA) approach for the analysis of COVID-19 in Saudi Arabia. We have proposed this approach and developed it in [1–3] for separability and grouping step in SSA, which plays an important role for reconstruction and forecasting in the SSA. The modified SSA mainly enables us to identify the number of the interpretable components required for separability, signal extraction and noise reduction. The approach was examined using different number of simulated and real data with different structures and signal to noise ratio. In this study we examine its capability in analysing COVID-19 data. Then, we use Vector SSA for predicting new data points and the peak of this pandemic. The results shows that the approach can be used as a promising one in decomposing and forecasting the daily cases of COVID-19 in Saudi Arabia.

scholarly journals Comparative Study between the Discrete-Frequency Kalman Filtering and the Discrete-Time Kalman Filtering with Application in Noise Reduction in Speech Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Leandro Aureliano da Silva ◽  
Gilberto Arantes Carrijo ◽  
Eduardo Silva Vasconcelos ◽  
Roberto Duarte Campos ◽  
Cleiton Silvano Goulart ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Comparative Study ◽  
Noise Reduction ◽  
Discrete Time ◽  
Kalman Filtering ◽  
Signal To Noise Ratio ◽  
Spectral Distortion ◽  
Signal To Noise ◽  
Discrete Frequency ◽  
Speech Reconstruction

This article aims to carry out a comparative study between discrete-time and discrete-frequency Kalman filters. In order to assess the performance of both methods for speech reconstruction, we measured the output segmental signal-to-noise ratio and the Itakura-Saito distance provided by each algorithm over 25 different voice signals. The results show that although the two algorithms performed very similarly regarding noise reduction, the discrete-time Kalman filter produced smaller spectral distortion on the estimated signals when compared with the discrete-frequency Kalman filter.

Adaptation of Telegraph Diffusion Equation for Noise Reduction on Images

2017 ◽  
Vol 17 (02) ◽  
pp. 1750010 ◽  
Author(s):  
Pandry Koffi Ghislain ◽  
Georges Lausanne Loum ◽  
Ouattara Nouho
Keyword(s):  
Diffusion Equation ◽  
Noise Reduction ◽  
Local Structure ◽  
Signal To Noise Ratio ◽  
Input Image ◽  
Weak Signal ◽  
Signal To Noise ◽  
Reduction Processes ◽  
Fine Structures

The Telegraph Diffusion Equation (TDE) used in some noise reduction processes in an image includes two main parameters: the damping coefficient and the relaxation time. Classically, the first is determined globally for a given input image, while the second one is set constant. In this paper, we propose to determine the values of these parameters according to the information and the image local structure. We then get an adaptive diffusion equation that permits to better control the degree of smoothness and preserve fine structures and image contours avoiding speckles phenomena and staircase. The acquired results show that the proposed method improves the quality of images that have a weak signal-to-noise ratio, comparatively to the methods based on the TDE whose parameters are not adaptive.

Embedded Sensing Design of Gearboxes for Vibration Monitoring

2010 ◽  
Vol 29-32 ◽  
pp. 264-268
Author(s):  
Z.S. Chen ◽  
Yong Min Yang ◽  
Z.X. Ge ◽  
C. Li
Keyword(s):  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Embedded Sensors ◽  
Analysis Method ◽  
Signal To Noise ◽  
Normal Functions ◽  
Embedded Sensing ◽  
Vibration Signal Analysis

Vibration signal analysis is one of the most effective ways for condition monitoring of gearboxes. Traditional way is often to mount additional accelerometer sensors on their cases, which has two unavoidable defects: signal-to-noise ratio is often low due to long signal travel paths and it may be not allowable due to space limitations. While embedded diagnostics (ED) can solve these two problems well by embedding sensors close to fault sources. However, embedded sensing design is a great challenge of ED because embedded sensors must have effects on the structure integrity of a gearbox. So it is necessary to determine how to embed sensors in order to ensure normal functions of a gearbox. In this paper, a finite element-based structure analysis method was proposed to perform embedded sensing design of bearings and gears to determine the optimal modified structure size.

scholarly journals Noise reduction with reflection supervirtual interferometry

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. V249-V256
Author(s):  
Kai Lu ◽  
Zhaolun Liu ◽  
Sherif Hanafy ◽  
Gerard Schuster
Keyword(s):  
Noise Reduction ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Data Set ◽  
Reflection Data ◽  
The Poor ◽  
The Earth ◽  
The Common ◽  
Noise Ratio

To image deeper portions of the earth, geophysicists must record reflection data with much greater source-receiver offsets. The problem with these data is that the signal-to-noise ratio (S/N) significantly diminishes with greater offset. In many cases, the poor S/N makes the far-offset reflections imperceptible on the shot records. To mitigate this problem, we have developed supervirtual reflection interferometry (SVI), which can be applied to far-offset reflections to significantly increase their S/N. The key idea is to select the common pair gathers where the phases of the correlated reflection arrivals differ from one another by no more than a quarter of a period so that the traces can be coherently stacked. The traces are correlated and summed together to create traces with virtual reflections, which in turn are convolved with one another and stacked to give the reflection traces with much stronger S/Ns. This is similar to refraction SVI except far-offset reflections are used instead of refractions. The theory is validated with synthetic tests where SVI is applied to far-offset reflection arrivals to significantly improve their S/N. Reflection SVI is also applied to a field data set where the reflections are too noisy to be clearly visible in the traces. After the implementation of reflection SVI, the normal moveout velocity can be accurately picked from the SVI-improved data, leading to a successful poststack migration for this data set.

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