An Experimental Rig for Damage Detection of Rolling Bearings

2005 ◽  
Vol 293-294 ◽  
pp. 509-516 ◽  
Author(s):  
Luigi Garibaldi ◽  
Alessandro Fasana ◽  
Stefano Marchesiello
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Testing Machine ◽  
Load Level ◽  
Test Rig ◽  
Signal To Noise ◽  
Rolling Bearings ◽  
Rotating Speed ◽  
Acceleration Data ◽  
Experimental Rig

This paper describes the design of a rig devoted to testing rolling bearings. In order to control the signal-to-noise ratio of the measures, a potentially very silent test rig has been designed and assembled, with the possibility of simply changing the bearing under investigation as well as its load level and rotating speed. Acceleration data have then been examined by means of envelope, wavelet transform and cyclostationarity analyses, showing that the testing machine fulfils its task.

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

scholarly journals Implementation of Spatial-Scale Domain Based De-Noising Techniques using Different Thresholding

2020 ◽  
Vol 9 (3) ◽  
pp. 3594-3603
Keyword(s):  
Wavelet Transform ◽  
Root Mean Square Error ◽  
Signal To Noise Ratio ◽  
Objective Evaluation ◽  
Contourlet Transform ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Stationary Wavelet Transform ◽  
Signal To Noise ◽  
Different Levels

This paper aims in presenting a thorough comparison of performance and usefulness of multi-resolution based de-noising technique. Multi-resolution based image denoising techniques overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques, as it provides the information of 2-Dimensional (2-D) signal at different levels and scales, which is desirable for image de-noising. The multiresolution based de-noising techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT), have been selected for the de-noising of camera images. Further, the performance of different denosing techniques have been compared in terms of different noise variances, thresholding techniques and by using well defined metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Analysis of result shows that shift-invariant NSCT technique outperforms the CT, SWT and DWT based de-noising techniques in terms of qualititaive and quantitative objective evaluation

scholarly journals An Image Steganography Algorithm using Fractional Discrete Wavelet Transform with Advanced Encryption System

2020 ◽  
Vol 9 (5) ◽  
pp. 541-546
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

The research constitutes a distinctive technique of steganography of image. The procedure used for the study is Fractional Random Wavelet Transform (FRWT). The contrast between wavelet transform and the aforementioned FRWT is that it comprises of all the benefits and features of the wavelet transform but with additional highlights like randomness and partial fractional value put up into it. As a consequence of the fractional value and the randomness, the algorithm will give power and a rise in the surveillance layers for steganography. The stegano image will be acquired after administrating the algorithm which contains not only the coated image but also the concealed image. Despite the overlapping of two images, any diminution in the grade of the image is not perceived. Through this steganographic process, we endeavor for expansion in surveillance and magnitude as well. After running the algorithm, various variables like Mean Square Error (MSE) and Peak Signal to Noise ratio (PSNR) are deliberated. Through the intended algorithm, a rise in the power and imperceptibility is perceived and it can also support diverse modification such as scaling, translation and rotation with algorithms which previously prevailed. The irrefutable outcome demonstrated that the algorithm which is being suggested is indeed efficacious.

Signal to Noise Ratio Enhancement Using Empirical Wavelet Transform

2019 ◽  
Author(s):  
Wei Yi Lee ◽  
Rosita Hamidi ◽  
Deva Ghosh ◽  
Mohd Hafiz Musa
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Empirical Wavelet Transform ◽  
Noise Ratio

scholarly journals MODELLING OF PARTIAL DISCHARGE ANALYSIS SYSTEM USING WAVELET TRANSFORM DENOISING TECHNIQUE IN LABVIEW ENVIRONMENT

2019 ◽  
Vol 81 (6) ◽  
Author(s):  
A. Nazifah Abdullah ◽  
S. H. K. Hamadi ◽  
M. Isa ◽  
B. Ismail ◽  
A. N. Nanyan ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Virtual Instrument ◽  
Signal To Noise Ratio ◽  
Partial Discharge ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Mother Wavelet ◽  
Signal To Noise ◽  
Labview Software ◽  
Analysis System

Partial discharge (PD) measurement is an essential to detect and diagnose the existence of the PD. However, this measurement has faced noise disturbance in industrial environments. Thus, PD analysis system using discrete wavelet transform (DWT) denoising technique via Laboratory Virtual Instrument Engineering Workbench (LabVIEW) software is proposed to distinguish noise from the measured PD signal. In this work, the performance of denoising process is analyzed based on calculated mean square error (MSE) and signal to noise ratio (SNR). The result is manipulated based on Haar, Daubechies, Coiflets, Symlets and Biorthogonal type of mother wavelet with different decomposition levels. From the SNR results, all types of the mother wavelet are suitable to be used in denoising technique since the value of SNR is in large positive value. Therefore, further studies were conducted and found out that db14, coif3, sym5 and bior5.5 wavelets with least MSE value are considered good to be used in the denoising technique. However, bior5.5 wavelet is proposed as the most optimum mother wavelet due to consistency of producing minimum value of MSE and followed by db14.

scholarly journals Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Shanshan Chen ◽  
Bensheng Qiu ◽  
Feng Zhao ◽  
Chao Li ◽  
Hongwei Du
Keyword(s):  
Wavelet Transform ◽  
Compressed Sensing ◽  
Discrete Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Discrete Wavelet ◽  
Translation Invariance ◽  
Signal To Noise ◽  
Reconstruction Methods ◽  
Noise Ratio

Compressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with fast iterative shrinkage/soft thresholding algorithm (FISTA) to efficiently reduce such visual artifacts. The CDDDT-DWT has the characteristics of shift invariance, high degree, and a good directional selectivity. In addition, FISTA has an excellent convergence rate, and the design of FISTA is simple. Compared with conventional CS-based reconstruction methods, the experimental results demonstrate that this novel approach achieves higher peak signal-to-noise ratio (PSNR), larger signal-to-noise ratio (SNR), better structural similarity index (SSIM), and lower relative error.

scholarly journals Application of continuous potential function stochastic resonance in early fault diagnosis of rolling bearings

2020 ◽  
Vol 53 (5-6) ◽  
pp. 767-777
Author(s):  
Xueping Ren ◽  
Jian Kang ◽  
Zhixing Li ◽  
Jianguo Wang
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Potential Function ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Signal To Noise ◽  
Rolling Bearings ◽  
Continuous Potential ◽  
Potential Parameters

The early fault signal of rolling bearings is very weak, and when analyzed under strong background noise, the traditional signal processing method is not ideal. To extract fault characteristic information more clearly, the second-order UCPSR method is applied to the early fault diagnosis of rolling bearings. The continuous potential function itself is a continuous sinusoidal function. The particle transition is smooth and the output is better. Because of its three parameters, the potential structure is more comprehensive and has more abundant characteristics. When the periodic signal, noise and potential function are the best match, the system exhibits better denoise compared to that of other methods. This paper discusses the influence of potential parameters on the motion state of particles between potential wells in combination with the potential parameter variation diagrams discussed. Then, the formula of output signal-to-noise ratio is derived to further study the relationships among potential parameters, and then the ant colony algorithm is used to optimize potential parameters in order to obtain the optimal output signal-to-noise ratio. Finally, an early weak fault diagnosis method for bearings based on the underdamped continuous potential stochastic resonance model is proposed. Through simulation and experimental verification, the underdamped continuous potential stochastic resonance results are compared with those of the time-delayed feedback stochastic resonance method, which proves the validity of the underdamped continuous potential stochastic resonance method.

scholarly journals Improvement of CO2-DIAL Signal-to-Noise Ratio Using Lifting Wavelet Transform

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2362 ◽  
Author(s):  
Chengzhi Xiang ◽  
Ge Han ◽  
Yuxin Zheng ◽  
Xin Ma ◽  
Wei Gong
Keyword(s):  
Wavelet Transform ◽  
Carbon Cycle ◽  
Signal To Noise Ratio ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Signal Denoising ◽  
Differential Absorption ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Lifting Wavelet

Atmospheric CO2 plays an important role in controlling climate change and its effect on the carbon cycle. However, detailed information on the dynamics of CO2 vertical mixing remains lacking, which hinders the accurate understanding of certain key features of the carbon cycle. Differential absorption lidar (DIAL) is a promising technology for CO2 detection due to its characteristics of high precision, high time resolution, and high spatial resolution. Ground-based CO2-DIAL can provide the continuous observations of the vertical profile of CO2 concentration, which can be highly significant to gaining deeper insights into the rectification effect of CO2, the ratio of respiration photosynthesis, and the CO2 dome in urban areas. A set of ground-based CO2-DIAL systems were developed by our team and highly accurate long-term laboratory experiments were conducted. Nonetheless, the performance suffered from low signal-to-noise ratio (SNR) in field explorations because of decreasing aerosol concentrations with increasing altitude and surrounding interference according to the results of our experiments in Wuhan and Huainan. The concentration of atmospheric CO2 is derived from the difference of signals between on-line and off-line wavelengths; thus, low SNR will cause the superimposition of the final inversion error. In such a situation, an efficient and accurate denoising algorithm is critical for a ground-based CO2-DIAL system, particularly in field experiments. In this study, a method based on lifting wavelet transform (LWT) for CO2-DIAL signal denoising was proposed. This method, which is an improvement of the traditional wavelet transform, can select different predictive and update functions according to the characteristics of lidar signals, thereby making it suitable for the signal denoising of CO2-DIAL. Experiment analyses were conducted to evaluate the denoising effect of LWT. For comparison, ensemble empirical mode decomposition denoising was also performed on the same lidar signal. In addition, this study calculated the coefficient of variation (CV) at the same altitude among multiple original signals within 10 min and then performed the same calculation on the denoised signal. Finally, high-quality signal of ground-based CO2-DIAL was obtained using the LWT denoising method. The differential absorption optical depths of the denoised signals obtained via LWT were calculated, and the profile distribution information of CO2 concentration was acquired during field detection by using our developed CO2-DIAL systems.

Frequency Domain Design Method of Wavelet Basis Based on Pulsar Signal

2020 ◽  
Vol 73 (6) ◽  
pp. 1223-1236
Author(s):  
Sihai You ◽  
Hongli Wang ◽  
Yiyang He ◽  
Qiang Xu ◽  
Lei Feng
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Design Method ◽  
Signal To Noise Ratio ◽  
Frequency Domain Analysis ◽  
Time Of Arrival ◽  
Frequency Noise ◽  
Wavelet Basis ◽  
Signal To Noise ◽  
Noise Ratio

During pulsar navigation, the high-frequency noise carried by the pulsar profile signal reduces the accuracy of the pulse TOA (Time of Arrival) estimation. At present, the main method to remove signal noise by using wavelet transform is to redesign the function of the threshold and level of wavelet transform. However, the signal-to-noise ratio and other indicators of the filtered signal need to be further optimised, so a more appropriate wavelet basis needs to be designed. This paper proposes a wavelet basis design method based on frequency domain analysis to improve the denoising effect of pulsar signals. This method first analyses the pulsar contour signal in the frequency domain and then designs a Crab pulsar wavelet basis (CPn, where n represents the wavelet basis length) based on its frequency domain characteristics. In order to improve the real-time performance of the algorithm, a wavelet lifting scheme is implemented. Through simulation, this method analyses the pulsar contour signal data at home and abroad. Results show the signal-to-noise ratio can be increased by 4 dB, the mean square error is reduced by 61% and the peak error is reduced by 45%. Therefore, this method has better filtering effect.

Selection of Optimistic Wavelet Base in Running-In Vibration Signal Denoising of Remanufacturing Engine

2012 ◽  
Vol 226-228 ◽  
pp. 335-339
Author(s):  
Xiang Bi An ◽  
Lei Chen ◽  
Cheng Fa Chen ◽  
Yun Chuan Bai ◽  
Chao Yang
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Mean Square ◽  
Wavelet Base ◽  
Signal To Noise ◽  
Wavelet Bases ◽  
The Impact ◽  
Selection Of

In the process of running-in vibration signal denoising for remanufacturing engine based on wavelet transform, there are relatively large differences in denoising results when different wavelet bases are used, so the selection of wavelet bases affect the consequent of de-noising processing. On the base of analyzing the characteristics of the commonly used wavelet bases, the paper has compared the impact of the wavelet base on signal denoising. Using SNR(Signal to Noise Ratio) and RMSE(Root Mean Square Error) as criteria, and combined with the characteristics of the running-in vibration signal of the remanufacturing engine, the wavelet base coif4 has been selected as the optimistic base, which has relatively better denoising effect, and improves the SNR and resolution, so it can be used in actual practice.

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