scholarly journals The Feature Extraction through Wavelet Coefficients of Metal Friction Noise for Adhesive and Abrasive Wear Monitoring

2021 ◽  
Vol 11 (9) ◽  
pp. 3755
Author(s):  
Yeonuk Seong ◽  
Donghyeon Lee ◽  
Jihye Yeom ◽  
Junhong Park
Keyword(s):  
Abrasive Wear ◽  
Metal Surfaces ◽  
Signal To Noise Ratio ◽  
Physical Phenomenon ◽  
Surface Condition ◽  
Strong Relationship ◽  
Wavelet Coefficients ◽  
Transform Method ◽  
Friction Noise ◽  
Acoustic Signature

Friction between metals is a physical phenomenon that occurs in manufacturing machine tools. This annoying noise implies unnecessary metal contact and deterioration of a mechanical system. In this study, for the monitoring of the friction between two metal surfaces, the acoustic signature was extracted by applying the wavelet transform method to the noise measured from the change in contact force for each state of adhesive and abrasive wear. Experiments were conducted with a constant relative speed between the contacting metal surfaces. For the adhesive wear, the peak signal-to-noise ratio (PSNR) calculated by the wavelet transformation increases with the increasing contact pressure. Opposite trends were observed for the abrasive wear. The proposed index formed a group within a specific range. This ratio exhibited a strong relationship with the wear characteristics and the surface condition. From the proposed index calculated by the wavelet coefficients, the continuous monitoring of the wear influence on the failure of the machine movement operations is achieved by the sound radiation from the contacting surfaces.

scholarly journals Cell-filtering-based multi-scale Shannon–Cosine wavelet denoising method for locust slice images

2019 ◽  
Vol 17 (05) ◽  
pp. 1950035 ◽  
Author(s):  
Shenghan Mei ◽  
Xiaochun Liu ◽  
Shuli Mei
Keyword(s):  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Similarity Index ◽  
Structural Similarity ◽  
Wavelet Denoising ◽  
Experimental Results ◽  
Transform Method ◽  
Denoising Method ◽  
Multi Scale ◽  
Piecewise Smoothness

The locust slice images have all the features such as strong self-similarity, piecewise smoothness and nonlinear texture structure. Multi-scale interpolation operator is an effective tool to describe such structures, but it cannot overcome the influence of noise on images. Therefore, this research designed the Shannon–Cosine wavelet which possesses all the excellent properties such as interpolation, smoothness, compact support and normalization, then constructing multi-scale wavelet interpolative operator, the operator can be applied to decompose and reconstruct the images adaptively. Combining the operator with the local filter operator (mean and median), a multi-scale Shannon–Cosine wavelet denoising algorithm based on cell filtering is constructed in this research. The algorithm overcomes the disadvantages of multi-scale interpolation wavelet, which is only suitable for describing smooth signals, and realizes multi-scale noise reduction of locust slice images. The experimental results show that the proposed method can keep all kinds of texture structures in the slice image of locust. In the experiments, the locust slice images with mixture noise of Gaussian and salt–pepper are taken as examples to compare the performances of the proposed method and other typical denoising methods. The experimental results show that the Peak Signal-To-Noise Ratio (PSNR) of the denoised images obtained by the proposed method is greater 27.3%, 24.6%, 2.94%, 22.9% than Weiner filter, wavelet transform method, median and average filtering, respectively; and the Structural Similarity Index (SSIM) for measuring image quality is greater 31.1%, 31.3%, 15.5%, 10.2% than other four methods, respectively. As the variance of Gaussian white noise increases from 0.02 to 0.1, the values of PSNR and SSIM obtained by the proposed method only decrease by 11.94% and 13.33%, respectively, which are much less than other 4 methods. This shows that the proposed method possesses stronger adaptability.

Random denoising and signal nonlinearity approach by time-frequency peak filtering using weighted frequency reassignment

Geophysics ◽  
2013 ◽  
Vol 78 (6) ◽  
pp. V229-V237 ◽  
Author(s):  
Hongbo Lin ◽  
Yue Li ◽  
Baojun Yang ◽  
Haitao Ma
Keyword(s):  
Weight Function ◽  
Instantaneous Frequency ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Signal To Noise ◽  
Transform Method ◽  
Time Frequency ◽  
Frequency Peak ◽  
Noise Ratio

Time-frequency peak filtering (TFPF) may efficiently suppress random noise and hence improve the signal-to-noise ratio. However, the errors are not always satisfactory when applying the TFPF to fast-varying seismic signals. We begin with an error analysis for the TFPF by using the spread factor of the phase and cumulants of noise. This analysis shows that the nonlinear signal component and non-Gaussian random noise lead to the deviation of the pseudo-Wigner-Ville distribution (PWVD) peaks from the instantaneous frequency. The deviation introduces the signal distortion and random oscillations in the result of the TFPF. We propose a weighted reassigned smoothed PWVD with less deviation than PWVD. The proposed method adopts a frequency window to smooth away the residual oscillations in the PWVD, and incorporates a weight function in the reassignment which sharpens the time-frequency distribution for reducing the deviation. Because the weight function is determined by the lateral coherence of seismic data, the smoothed PWVD is assigned to the accurate instantaneous frequency for desired signal components by weighted frequency reassignment. As a result, the TFPF based on the weighted reassigned PWVD (TFPF_WR) can be more effective in suppressing random noise and preserving signal as compared with the TFPF using the PWVD. We test the proposed method on synthetic and field seismic data, and compare it with a wavelet-transform method and [Formula: see text] prediction filter. The results show that the proposed method provides better performance over the other methods in signal preserving under low signal-to-noise ratio.

Influence of Grain Refining on Abrasive Wear of Submicrocrystalline Al-Si Alloys

2011 ◽  
Vol 674 ◽  
pp. 97-103 ◽  
Author(s):  
Henryk Dybiec ◽  
Maciej Motyka
Keyword(s):  
Mechanical Properties ◽  
Abrasive Wear ◽  
Considerable Increase ◽  
Surface Condition ◽  
Grain Refining ◽  
Friction Coefficients ◽  
Wear And Friction ◽  
Available Information ◽  
Submicrocrystalline Materials ◽  
Wear Tests

Light weight nano/submicrocrystalline materials are promising group of constructional materials combining low density with high mechanical properties. However, their potential application requires extensive testing of functional properties, e.g. tribological ones, which may be significant and determine their practical use. Available information on abrasive wear and friction coefficients in nano/submicrocrystalline materials is rather poor. Therefore the aim of this paper is to fill the gap in the literature in this field. The AlSi12Fe5Cu3Mg alloy (RS422) produced by rapid solidification and plastic consolidation with grain size of basic phase components in the range from 50 nm to 300 nm was examined. Microstructure and mechanical properties of the materials were determined. Abrasive wear tests, static and kinematics friction coefficients measurement were carried out under the surface condition including dry, wet and oil lubricant. The results have been compared to the values of similar quantities determined in the same conditions for conventionally produced alloy AlSi11FeCuMn (AK11). Substantial increase of friction coefficients for RS442 comparing to AlSi11FeCuMn material was found, however, abrasive wear for nano/submicron grained materials were low in comparison to conventional one. Considerable increase of abrasive wear at water presence and very weak attrition at oil lubrication was observed. Relationship between structure and mechanical properties of tested materials was analyzed.

scholarly journals A review of various image fusion types and transforms

2021 ◽  
Vol 24 (3) ◽  
pp. 1515
Author(s):  
Ayodeji Olalekan Salau ◽  
Shruti Jain ◽  
Joy Nnenna Eneh
Keyword(s):  
Image Fusion ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Transform Method ◽  
Important Approach ◽  
Fusion Applications ◽  
Noise Ratio ◽  
Ihs Transform ◽  
Validation Of Results

Utilizing multiple views of an image is an important approach in digital photography, video editing, and medical image fusion applications. Image fusion (ImF) methods are used to improve an image's quality and remove noise from the image signal, resulting in a higher signal-to-noise ratio. A complete assessment of the literature on the different transform kinds, techniques, and rules utilized in ImF is presented in this paper. To assess the outcomes, a white flower image was fused using discrete wavelet transform (DWT) and discrete cosine transform (DCT) techniques. For validation of results, the red, green, blue (RGB) and intensity hue saturation (IHS) values of individual and fused images were evaluated. The results obtained from the fused images with the spatial IHS transform method give a remarkable performance. Furthermore, the results of the performance evaluation using DWT and DCT fusion techniques show that the same peak signal to noise ratio (PSNR) of 114.04 was achieved for both PSNR 1 and PSNR 2 for DCT, and different results were obtained for DWT. For signal to noise ratio (SNR), SNR 1 and SNR 2 achieved slightly similar values of 114.00 and 114.01 for DCT, while a SNR of 113.28 and 112.26 was achieved for SNR 1 and SNR 2 respectively.

scholarly journals ON ESTIMATES OF COEFFICIENTS OF GENERALIZED ATOMIC WAVELETS EXPANSIONS AND THEIR APPLICATION TO DATA PROCESSING

2020 ◽  
pp. 44-57 ◽  
Author(s):  
Victor Olexandrovych Makarichev ◽  
Vladimir Vasilyevich Lukin ◽  
Iryna Victorivna Brysina
Keyword(s):  
Digital Image ◽  
Digital Images ◽  
Signal To Noise Ratio ◽  
Expected Value ◽  
Wavelet Expansion ◽  
Correction Coefficient ◽  
Quality Loss ◽  
Wavelet Coefficients ◽  
Absolute Deviation ◽  
Real Value

Discrete atomic compression (DAC) of digital images is considered. It is a lossy compression algorithm. The aim of this paper is to obtain a mechanism for control of quality loss. Among a large number of different metrics, which are used to assess loss of quality, the maximum absolute deviation or the MAD-metric is chosen, since it is the most sensitive to any even the most minor changes of processed data. In DAC, the main loss of quality is got in the process of quantizing atomic wavelet coefficients that is the subject matter of this paper. The goal is to investigate the effect of the quantization procedure on atomic wavelet coefficients. We solve the following task: to obtain estimates of these coefficients. In the current research, we use the methods of atomic function theory and digital image processing. Using the properties of the generalized atomic wavelets, we get  estimates of generalized atomic wavelet expansion coefficients. These inequalities provide dependence of quality loss measured by the MAD-metric on the parameters of quantization in the form of upper bounds. They are confirmed by the DAC-processing of the test images. Also, loss of quality measured by root mean square (RMS) and peak signal to noise ratio (PSNR) is computed. Analyzing the results of experiments, which are carried out using the computer program "Discrete Atomic Compression: Research Kit", we obtain the following results: 1) the deviation of the expected value of MAD from its real value in some cases is large; 2) accuracy of the estimates depends on parameters of quantization, as well as depth of atomic wavelet expansion and type of the digital image (full color or grayscale); 3) discrepancies can be reduced by applying a correction coefficient; 4) the ratio of the expected value of MAD to its real value behaves relatively constant and the ratio of the expected value of MAD to RMS and PSNR do not. Conclusions: discrete atomic compression of digital images in combination with the proposed method of quality loss control provide obtaining results of the desired quality and its further development, research and application are promising.

scholarly journals Wavelet Compressive Sampling Signal Reconstruction Using Upside-Down Tree Structure

2011 ◽  
Vol 2011 ◽  
pp. 1-10
Author(s):  
Yijiu Zhao ◽  
Xiaoyan Zhuang ◽  
Zhijian Dai ◽  
Houjun Wang
Keyword(s):  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Compressive Sampling ◽  
Tree Structure ◽  
Orthogonal Matching Pursuit ◽  
Reconstruction Method ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
Signal To Noise

This paper suggests an upside-down tree-based orthogonal matching pursuit (UDT-OMP) compressive sampling signal reconstruction method in wavelet domain. An upside-down tree for the wavelet coefficients of signal is constructed, and an improved version of orthogonal matching pursuit is presented. The proposed algorithm reconstructs compressive sampling signal by exploiting the upside-down tree structure of the wavelet coefficients of signal besides its sparsity in wavelet basis. Compared with conventional greedy pursuit algorithms: orthogonal matching pursuit (OMP) and tree-based orthogonal matching pursuit (TOMP), signal-to-noise ratio (SNR) using UDT-OMP is significantly improved.

An Adaptive Wavelet Shrinkage and its Application in Image De-Noising

2011 ◽  
Vol 128-129 ◽  
pp. 500-503
Author(s):  
Tian Jie Cao
Keyword(s):  
Linear Function ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Computing Time ◽  
Adaptive Method ◽  
Risk Estimate ◽  
Wavelet Coefficients ◽  
Adaptive Wavelet ◽  
Proportional Relation ◽  
Unbiased Risk

In this paper an adaptive method of shrinkage of the wavelet coefficients is presented. In the method, the wavelet coefficients are divided into two classes by a threshold. One class of them with the smaller absolute values at a scale is transformed with a proportional relation，another class with the larger absolute values at the same scale is transformed with a linear function. The threshold and the coefficient in the proportional relation or in the linear function are determined by the principle of minimizing the Stein’s unbiased risk estimate. In the paper, the method of estimation of the threshold and the coefficient is given and the adaptive method of shrinkage of the wavelet coefficients is applied to image denoising. Examples in the paper show that the presented method has an advantage over SureShrink from the point of view of both the Stein’s unbiased risk estimate and the signal-to-noise ratio. In addition, the method takes almost the same computing time as the SureShrink in image denoising.

EFFICIENT STATISTICAL MODELING OF WAVELET COEFFICIENTS FOR IMAGE DENOISING

2009 ◽  
Vol 07 (05) ◽  
pp. 629-641 ◽  
Author(s):  
YING CHEN ◽  
ZHI-CHENG JI ◽  
CHUN-JIAN HUA
Keyword(s):  
Statistical Modeling ◽  
Variance Estimation ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Computational Cost ◽  
Estimation Procedure ◽  
Critical Issue ◽  
Wavelet Coefficients ◽  
Minimum Mean Squared Error ◽  
Wavelet Image

Statistical modeling of wavelet coefficients is a critical issue in wavelet domain signal processing. By analyzing the defects of other existing methods, and exploiting the local dependency of wavelet coefficients, an efficient statistical model is proposed. Improved variance estimation of the local wavelet coefficients can be obtained using the new model. Then we apply an approximate minimum mean squared error (MMSE) estimation procedure to restore the wavelet image coefficients. The modeling process is computational cost saving, and the denoising experiments show the algorithm outperforms other approaches in peak-signal-to-noise ratio (PSNR).

IMAGE DENOISING BASED ON WAVELET SHRINKAGE USING NEIGHBOR AND LEVEL DEPENDENCY

2009 ◽  
Vol 07 (03) ◽  
pp. 299-311 ◽  
Author(s):  
DONGWOOK CHO ◽  
TIEN D. BUI ◽  
GUANGYI CHEN
Keyword(s):  
Signal To Noise Ratio ◽  
Signal Denoising ◽  
Wavelet Thresholding ◽  
Wavelet Coefficients ◽  
Wavelet Shrinkage ◽  
Minimum Risk ◽  
Shrinkage Methods ◽  
Risk Estimator ◽  
Visual Artifacts ◽  
Unbiased Risk

Since Donoho et al. proposed the wavelet thresholding method for signal denoising, many different denoising approaches have been suggested. In this paper, we present three different wavelet shrinkage methods, namely NeighShrink, NeighSure and NeighLevel. NeighShrink thresholds the wavelet coefficients based on Donoho's universal threshold and the sum of the squares of all the wavelet coefficients within a neighborhood window. NeighSure adopts Stein's unbiased risk estimator (SURE) instead of the universal threshold of NeighShrink so as to obtain the optimal threshold with minimum risk for each subband. NeighLevel uses parent coefficients in a coarser level as well as neighbors in the same subband. We also apply a multiplying factor for the optimal universal threshold in order to get better denoising results. We found that the value of the constant is about the same for different kinds and sizes of images. Experimental results show that our methods give comparatively higher peak signal to noise ratio (PSNR), are much more efficient and have less visual artifacts compared to other methods.

Application of Tonal Tracking to Ship Acoustic Signature Feature Identification

2011 ◽  
Vol 133 (6) ◽  
Author(s):  
M. L. Seto
Keyword(s):  
Signal To Noise Ratio ◽  
Background Level ◽  
Feature Identification ◽  
Signal To Noise ◽  
Detection Range ◽  
Detection And Tracking ◽  
Acoustic Signature ◽  
Intensity Changes ◽  
Noise Ratio ◽  
The Impact

A ship’s radiated acoustic signature is known after a range measurement, but it changes from that the longer the ship is in-service. The Ship Signatures Management System (SSMS) provides an organic, real-time capability for a naval ship to monitor its own signature in order to evaluate the impact of proposed actions on its counter-detection range and sensor performance. Ship protection is enhanced through insightful and timely signature data. In particular, this paper discusses the tonal detection and tracking algorithms used to monitor on-board machinery and propeller activity. The paper specifically addresses tonals that appear or disappear as a consequence of changes in the background level, as well as that of crossed tonals. This is of significance because it impacts the SSMS’s ability to attribute cause to changes in the ship acoustic signature. In particular, it is impossible to associate tonals that are time synchronized in their frequency and intensity changes as being created by a single cause (e.g., piece of machinery) with a known tonal set. The use of tonal amplitude and the cause for the signal-to-noise ratio change, in addition to the signal-to-noise ratio, remedies the detection and tracking of tonals that appear/disappear relative to the background. The additional use of tonal width is suggested as a means to remedy the problem of crossed tonals.

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