scholarly journals Repetitive Transient Extraction Using the Optimized SES Entropy Wavelet for Fault Diagnosis of Rotating Machinery

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tingzhong Wang ◽  
Lingli Zhu ◽  
Miaomiao Fu ◽  
Tingting Zhu ◽  
Ping He
Keyword(s):  
Fault Diagnosis ◽  
Impulsive Noise ◽  
Signal To Noise Ratio ◽  
Morlet Wavelet ◽  
Harsh Environment ◽  
Wavelet Coefficients ◽  
Signal To Noise ◽  
Central Frequency ◽  
Wavelet Method ◽  
Vibration Data

Repetitive transients are usually generated in the monitoring data when a fault occurs on the machinery. As a result, many methods such as kurtogram and optimized Morlet wavelet and kurtosis method are proposed to extract the repetitive transients for fault diagnosis. However, one shortcoming of these methods is that they are constructed based on the index of kurtosis and are sensitive to the impulsive noise, leading to failure in accurately diagnosing the fault of the machinery operating under harsh environment. To address this issue, an optimized SES entropy wavelet method is proposed. In the proposed method, the optimized parameters including bandwidth and central frequency of Morlet wavelets are selected. Then, based on the wavelet coefficients decomposed using the optimized Morlet wavelet, the SES entropy is calculated to select the scales of wavelet coefficients. Finally, the repetitive transients are reconstructed based on the denoising wavelet coefficients of the selected scales. One simulation case and vibration data collected from the experimental setup are used to verify the effectiveness of the proposed method. The simulated and experimental analyses showed that the signal-to-noise ratio (SNR) of the proposed method has the largest value. Specifically, the SNR in the experimental analysis of the proposed method is 0.6, while that of the other three methods is 0.043, 0.0065, and 0.0045, respectively. Therefore, the result shows that the proposed method is superior to the traditional methods for repetitive transient extraction from the vibration data suffered from impulsive noise.

Multi-Wavelet Application Based on Engineering Building Deformation Monitoring

2013 ◽  
Vol 353-356 ◽  
pp. 3420-3423
Author(s):  
Yong Hong He ◽  
Peng Wei Jin
Keyword(s):  
Signal Processing ◽  
Signal To Noise Ratio ◽  
Small Deformation ◽  
Deformation Monitoring ◽  
Biorthogonal Wavelet ◽  
Signal To Noise ◽  
Actual Problem ◽  
Wavelet Method ◽  
Noise Ratio

In order to separate effectively small deformation from GPS signal containing the error, Based on multiwavelet theory, the paper makes DGHM for GPS deformation monitoring signal processing, because the DGHM biorthogonal wavelet is with short support, orthogonality, symmetry ( antisymmetry ) and two approximation and other characteristics, in the same length of filtering, multi-wavelet method is more superior than traditional wavelet method, improves the signal to noise ratio, obtains higher accuracy, confirms correct and practical in the actual problem, the algorithm has opened a new way for deformation monitoring signal processing

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 PERFORMANCE EVALUATION OF SINGLE CARRIER FREQUENCY DOMAIN EQUILIZER (SC-FDE) OVER IMPULSIVE NOISE CHANNEL

2019 ◽  
Vol 11 (2) ◽  
pp. 270-277
Author(s):  
Hussein Abdullah Leftah ◽  
Husham Lateef Swadi
Keyword(s):  
Frequency Domain ◽  
Carrier Frequency ◽  
Communication Systems ◽  
Impulsive Noise ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Signal To Noise ◽  
Single Carrier ◽  
Noise Ratio ◽  
Error Floors

Impulsive noise is considered as one of the major source of disturbance in the state-of-the-art multicarrier (MC) communication systems. Therefore, several techniques are being constantly proposed to eliminate the effect of such noise. In this work, a time domain matrix interleaved is compiled with a single carrier frequency domain equalizer (SC-FDE) is proposed to reduce the deleterious effects of impulsive noise. A mathematical model for the proposed scheme is also presented in this paper. Simulation results show that the proposed technique superiors the interleaved multicarrier system where the proposed scheme can completely avoid the error floors not only at high signal-to-noise ratio (SNR) but also at heavily distributed impulsive noise. The bit-error-rate (BER) of the alternative proposed scheme decreases as the signal-to-noise ratio (SNR) increases whereas the BER of the standard system suffers from error-floors with a constant BER at about 10-3 for about 8 dB SNR for medium and heavily impulsive noise.

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 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.

Application of the Dither Technology in a Fault Detection System

2013 ◽  
Vol 281 ◽  
pp. 71-74
Author(s):  
Na Chen ◽  
Shao Pu Yang ◽  
Cun Zhi Pan
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Detection System ◽  
Signal To Noise Ratio ◽  
Sensor Signal ◽  
Signal To Noise ◽  
Sampling System ◽  
System A ◽  
Fault Diagnosis System ◽  
Direct Guidance

In a fault detection system A/D conversion is a necessary step, in which quantization issues are unavoidable. Problems about quantization effects can be solved properly by using the dither technique. Firstly quantization problems of A/D conversion in a fault diagnosis system are discussed. Then the principle of dithering technique is introduced from the view of probability statistics. In further more, it is tested that fault signals whose amplitude is less than the quantization interval can be extracted, and that coherent harmonic interference in quantizing process can also be eliminated. Finally the result shows that by using dither technique the system can gain an enhanced level of fault detection with a faint signal-to-noise ratio loss, which has a direct guidance on engineering design in sensor-signal-sampling system.

A Bayesian Multiresolution Approach for Noise Removal in Medical Magnetic Resonance Images

2018 ◽  
Vol 29 (1) ◽  
pp. 189-201 ◽  
Author(s):  
Sima Sahu ◽  
Harsh Vikram Singh ◽  
Basant Kumar ◽  
Amit Kumar Singh
Keyword(s):  
Magnetic Resonance ◽  
Bayesian Approach ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
White Gaussian Noise ◽  
Error Estimator ◽  
Wavelet Coefficients ◽  
Signal To Noise ◽  
Noise Ratio

Abstract A Bayesian approach using wavelet coefficient modeling is proposed for de-noising additive white Gaussian noise in medical magnetic resonance imaging (MRI). In a parallel acquisition process, the magnetic resonance image is affected by white Gaussian noise, which is additive in nature. A normal inverse Gaussian probability distribution function is taken for modeling the wavelet coefficients. A Bayesian approach is implemented for filtering the noisy wavelet coefficients. The maximum likelihood estimator and median absolute deviation estimator are used to find the signal parameters, signal variances, and noise variances of the distribution. The minimum mean square error estimator is used for estimating the true wavelet coefficients. The proposed method is simulated on MRI. Performance and image quality parameters show that the proposed method has the capability to reduce the noise more effectively than other state-of-the-art methods. The proposed method provides 8.83%, 2.02%, 6.61%, and 30.74% improvement in peak signal-to-noise ratio, structure similarity index, Pratt’s figure of merit, and Bhattacharyya coefficient, respectively, over existing well-accepted methods. The effectiveness of the proposed method is evaluated by using the mean squared difference (MSD) parameter. MSD shows the degree of dissimilarity and is 0.000324 for the proposed method, which is less than that of the other existing methods and proves the effectiveness of the proposed method. Experimental results show that the proposed method is capable of achieving better signal-to-noise ratio performance than other tested de-noising methods.

Application of Wavelet Method on Preprocessing of Voltage Signals

2010 ◽  
Vol 40-41 ◽  
pp. 272-276
Author(s):  
Li Di Wang ◽  
Nan Zhu ◽  
Jin Kai Li
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Electrical Parameter ◽  
Wavelet Denoising ◽  
Signal To Noise ◽  
Denoising Method ◽  
Wavelet Method ◽  
Squared Error ◽  
Noise Ratio

Wavelet denoising method is applied in the measurement voltage signals in this paper. Noise reduction is important for signal preprocessing in order to achieve many objects such as the improvement of accuracy of modal analysis and electrical parameter identification, the effective extraction of features and auto-matic classification of different kinds of signals. The voltage signals measured from one 35Kv bus are used for the preprocessing research. The denoising effect is evaluated by three parameters, i.e. signal to noise ratio, mean squared error, and capture ability of step points. Compared with the traditional methods including mean filtering and medial filtering, wavelet method is superior in signal to noise ratio and mean squared error.

scholarly journals Reduction of Impulsive Noise from Speech and Audio Signals by using Sd-Rom Algorithm

2021 ◽  
Vol 10 (1) ◽  
pp. 265-268
Author(s):  
G.Manmadha Rao* ◽  
Raidu Babu D.N ◽  
Krishna Kanth P.S.L ◽  
Vinay B. ◽  
Nikhil V.
Keyword(s):  
Impulse Noise ◽  
Impulsive Noise ◽  
Rank Order ◽  
Signal To Noise Ratio ◽  
Audio Signal ◽  
Window Size ◽  
Mean Value ◽  
Audio Signals ◽  
Signal To Noise ◽  
Noise Ratio

Removal of noise is the heart for speech and audio signal processing. Impulse noise is one of the most important noise which corrupts different parts in speech and audio signals. To remove this type of noise from speech and audio signals the technique proposed in this work is signal dependent rank order mean (SD-ROM) method in recursive version. This technique is used to replace the impulse noise samples based on the neighbouring samples. It detects the impulse noise samples based on the rank ordered differences with threshold values. This technique doesn’t change the features and tonal quality of signal. Rank ordered differences is used for detecting the impulse noise samples in speech and audio signals. Once the sample is detected as corrupted sample, that sample is replaced with rank ordered mean value and this rank ordered mean value depends on the sliding window size and neighbouring samples. This technique shows good results in terms of signal to noise ratio (SNR) and peak signal to noise ratio (PSNR) when compared with other techniques. It mainly used for removal of impulse noises from speech and audio signals.

An Image De-Noising Method Using Directions of Wavelet Decomposition Sub-Bands

2011 ◽  
Vol 130-134 ◽  
pp. 3058-3061
Author(s):  
Zheng Cai ◽  
Shao Hua Tao
Keyword(s):  
Wavelet Transform ◽  
Small Area ◽  
Wavelet Decomposition ◽  
Signal To Noise Ratio ◽  
Wavelet Coefficients ◽  
Signal To Noise ◽  
Image Information ◽  
Noise Ratio

In this paper, an image de-noising method using directions of wavelet decomposition sub-bands is proposed. Wavelet coefficients are correlated in a small area, and the wavelet transform uses wavelet coefficients in three directions to describe image information, so in each sub-band the proposed method only calculates neighboring wavelet coefficients in a certain direction rather than in eight directions. Compared with other wavelet de-noising methods, the proposed method can achieve higher peak signal to noise ratio.

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