scholarly journals A Novel Blind Deconvolution Method with Adaptive Period Estimation Technique and Its Application to Fault Feature Enhancement of Bearing

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Qiuyang Zhou ◽  
Cai Yi ◽  
Chenguang Huang ◽  
Jianhui Lin
Keyword(s):  
Experimental Data ◽  
Blind Deconvolution ◽  
Necessary Conditions ◽  
Input Parameter ◽  
Fault Recovery ◽  
Estimation Technique ◽  
Deconvolution Method ◽  
Application Range ◽  
Period Estimation ◽  
Envelope Signal

Minimum correlated generalized Lp/Lq deconvolution (MCG-Lp/Lq-D) is an important tool to detect periodic impulses in vibration mixture. It is proved to be a more stable technique than maximum correlated kurtosis deconvolution (MCKD) to recover the fault impulse under strong noise conditions. However, MCG-Lp/Lq-D still has limitations. One of the necessary conditions for the success of MCG-Lp/Lq-D is to provide a precise period of fault. An imprecise prior period will lead to performance degradation or even failure of the method. Therefore, in this paper, a MCG-Lp/Lq-D with adaptive fault period estimation capability is proposed, adaptive minimum correlated generalized Lp/Lq deconvolution (AMCG-Lp/Lq-D). The proposed method uses the autocorrelation function of envelope signal to estimate the fault period adaptively in each iteration and then takes the estimated period as the input parameter of MCG-Lp/Lq-D for the next iteration optimization. The proposed method does not require precise prior fault period input, which greatly improves the fault recovery accuracy and application range of MCG-Lp/Lq-D. Eventually, simulated and experimental data verify the effectiveness and superiority of AMCG-Lp/Lq-D.

scholarly journals An SNR Estimation Technique Based on Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1139 ◽  
Author(s):  
Kai Yang ◽  
Zhitao Huang ◽  
Xiang Wang ◽  
Fenghua Wang
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Intermediate Frequency ◽  
Estimation Technique ◽  
Signal To Noise ◽  
Snr Estimation ◽  
Application Range ◽  
Noise Ratio ◽  
Priori Information

Signal-to-noise ratio (SNR) is a priori information necessary for many signal processing algorithms or techniques. However, there are many problems exsisting in conventional SNR estimation techniques, such as limited application range of modulation types, narrow effective estimation range of signal-to-noise ratio, and poor ability to accommodate non-zero timing offsets and frequency offsets. In this paper, an SNR estimation technique based on deep learning (DL) is proposed, which is a non-data-aid (NDA) technique. Second and forth moment (M2M4) estimator is used as a benchmark, and experimental results show that the performance and robustness of the proposed method are better, and the applied ranges of modulation types is wider. At the same time, the proposed method is not only applicable to the baseband signal and the incoherent signal, but can also estimate the SNR of the intermediate frequency signal.

A novel blind deconvolution method and its application to fault identification

2019 ◽  
Vol 460 ◽  
pp. 114900 ◽  
Author(s):  
Yao Cheng ◽  
Bingyan Chen ◽  
Guiming Mei ◽  
Zhiwei Wang ◽  
Weihua Zhang
Keyword(s):  
Blind Deconvolution ◽  
Fault Identification ◽  
Deconvolution Method

Energy Loss for Swift Heavy Ions in Different Elemental Absorbers: A Different Approach for Effective Charge Parameterization

2015 ◽  
Vol 238 ◽  
pp. 196-205
Author(s):  
B. Rani ◽  
Kalpana Sharma ◽  
Neetu ◽  
Anupam ◽  
Shyam Kumar ◽  
...  
Keyword(s):  
Experimental Data ◽  
Energy Loss ◽  
Effective Charge ◽  
Heavy Ions ◽  
Close Agreement ◽  
Input Parameter ◽  
Quantum Mechanical ◽  
Swift Heavy Ions ◽  
Calculated Energy ◽  
Semi Empirical

The energy loss for swift heavy ions, covering Z=3-29(~0.2 - 5.0MeV/n), has been calculated in the elemental absorbers like C, Al and Ti. The present calculations are based on Bohr’s approach applicable in both classical and quantum mechanical regimes. The major input parameter, the effective charge, has been calculated in a different way without any empirical/semi-empirical parameterization. The calculated energy loss values have been compared with the available experimental data which results in a close agreement.

A novel geometry for SPECT imaging associated with the EM-type blind deconvolution method

1998 ◽  
Vol 45 (4) ◽  
pp. 2095-2101 ◽  
Author(s):  
Y.-H. Liu ◽  
A. Rangarajan ◽  
D. Gagnon ◽  
M. Therrien ◽  
A.J. Sinusas ◽  
...  
Keyword(s):  
Blind Deconvolution ◽  
Spect Imaging ◽  
Deconvolution Method

Analysis of an Adaptive Strain Estimation Technique in Elastography

2002 ◽  
Vol 24 (2) ◽  
pp. 109-118 ◽  
Author(s):  
S. Srinivasan ◽  
F. Kallel ◽  
R. Souchon ◽  
J. Ophir
Keyword(s):  
Experimental Data ◽  
Quantitative Analysis ◽  
Quantitative Comparison ◽  
Estimation Technique ◽  
Echo Signal ◽  
Initial Displacement ◽  
Estimation Techniques ◽  
Strain Estimation ◽  
Proof Of Principle

Elastography is based on the estimation of strain due to tissue compression or expansion. Conventional elastography involves computing strain as the gradient of the displacement (time-delay) estimates between gated pre- and postcompression signals. Uniform temporal stretching of the postcompression signals has been used to reduce the echo-signal decorrelation noise. However, a uniform stretch of the entire postcompression signal is not optimal in the presence of strain contrast in the tissue and could result in loss of contrast in the elastogram. This has prompted the use of local adaptive stretching techniques. Several adaptive strain estimation techniques using wavelets, local stretching and iterative strain estimation have been proposed. Yet, a quantitative analysis of the improvement in quality of the strain estimates over conventional strain estimation techniques has not been reported. We propose a two-stage adaptive strain estimation technique and perform a quantitative comparison with the conventional strain estimation techniques in elastography. In this technique, initial displacement and strain estimates using global stretching are computed, filtered and then used to locally shift and stretch the postcompression signal. This is followed by a correlation of the shifted and stretched postcompression signal with the precompression signal to estimate the local displacements and hence the local strains. As proof of principle, this adaptive stretching technique was tested using simulated and experimental data.

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