Dual-Mean Extraction Method of Dynamic Spectrum for Suppressing Random Noise and Coarse Error

This paper researches fault feature extraction method based on singular value decomposition and the improved HHT method for non-stationary characteristics of wind turbine gearbox vibration signal. Firstly, through the signal phase space reconstruction, the singular value decomposition as a pre-filter, to preprocessing the signal, effectively weaken the random noise. Then using EEMD to improve the HHT method, decompose the denoising signal into a series of different time scales component of intrinsic mode functions. The fault characteristics of the signal are extracted by the Hilbert transform. Finally, simulating gearbox fault experiment to verify the effectively of the proposed method.

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An Optimizing Dynamic Spectrum Differential Extraction Method for Noninvasive Blood Component Analysis

Applied Spectroscopy ◽

10.1177/0003702818815508 ◽

2019 ◽

Vol 74 (1) ◽

pp. 23-33 ◽

Cited By ~ 5

Author(s):

Wei Tang ◽

Qiang Chen ◽

Wenjuan Yan ◽

Guoquan He ◽

Gang Li ◽

...

Keyword(s):

Neural Network ◽

Correlation Coefficient ◽

Extraction Method ◽

Rbf Neural Network ◽

Extraction Methods ◽

Dynamic Spectrum ◽

The Other ◽

Single Trial ◽

Differential Extraction ◽

Better Than

Dynamic spectra (DS) can greatly reduce the influence of individual differences and the measurement environment by extracting the absorbance of pulsating blood at multiple wavelengths, and it is expected to achieve noninvasive detection of blood components. Extracting high-quality DS is the prerequisite for improving detection accuracy. This paper proposed an optimizing differential extraction method in view of the deficiency of existing extraction methods. In the proposed method, the sub-dynamic spectrum (sDS) is composed by sequentially extracting the absolute differences of two sample points corresponding to the height of the half peak on the two sides of the lowest point in each period of the logarithm photoplethysmography signal. The study was based on clinical trial data from 231 volunteers. Single-trial extraction method, original differential extraction method, and optimizing differential extraction method were used to extract DS from the volunteers’ experimental data. Partial least squares regression (PLSR) and radial basis function (RBF) neural network were used for modeling. According to the effect of PLSR modeling, by extracting DS using the proposed method, the correlation coefficient of prediction set ( Rp) has been improved by 17.33% and the root mean square error of prediction set has been reduced by 7.10% compared with the original differential extraction method. Compared with the single-trial extraction method, the correlation coefficient of calibration set ( Rc) has increased from 0.747659 to 0.8244, with an increase of 10.26%, while the correlation coefficient of prediction set ( Rp) decreased slightly by 3.22%, much lower than the increase of correction set. The result of the RBF neural network modeling also shows that the accuracy of the optimizing differential method is better than the other two methods both in calibration set and prediction set. In general, the optimizing differential extraction method improves the data utilization and credibility compared with the existing extraction methods, and the modeling effect is better than the other two methods.

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Dynamic Spectrum Extraction Method Based on Absolute Difference Summation and Statistical Theory

Journal of Applied Spectroscopy ◽

10.1007/s10812-019-00759-8 ◽

2019 ◽

Vol 85 (6) ◽

pp. 1058-1063

Author(s):

G. Li ◽

H. L. Wang ◽

M. Zhou ◽

Y. Peng ◽

L. Lin

Keyword(s):

Statistical Theory ◽

Extraction Method ◽

Dynamic Spectrum ◽

Absolute Difference

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A Feature Extraction Method Based on VMD and RDT and Its Application in Engine Crankshaft Bearing Fault

10.20944/preprints201803.0266.v1 ◽

2018 ◽

Author(s):

Gang Ren ◽

Jide Jia ◽

Jianmin Mei ◽

Xiangyu Jia ◽

Jiajia Han

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Random Noise ◽

Permutation Entropy ◽

Noise Robustness ◽

Feature Extraction Method ◽

Bearing Fault ◽

Random Decrement Technique ◽

Mode Decomposition ◽

Random Decrement

The vibration signal of the engine contains strong background noise and many kinds of modulating components, which is difficult to diagnose. Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). VMD can effectively avoid endpoint effect and modal aliasing. However, VMD cannot effectively eliminate the random noise in the signal, so the random decrement technique is introduced to solve the problem. Based on the crankshaft bearing fault simulation experiment, the four kinds of wear state vibration signals are decomposed by VMD, and the modal components with smaller permutation entropy are selected as fault components. Then the fault component is processed by the random decrement technique, and the Hilbert envelope spectrum of the fault component is obtained. Compared with the fault feature extraction method based on EMD and EEMD, the feature extraction results of the proposed method are better than those of the above two methods. The simulation analysis and the simulation test of the crankshaft bearing fault verify the effectiveness of the proposed method.

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Dynamic spectrum extraction method based on independent component analysis combined dual-tree complex wavelet transform

RSC Advances ◽

10.1039/c6ra28647j ◽

2017 ◽

Vol 7 (18) ◽

pp. 11198-11205 ◽

Cited By ~ 9

Author(s):

Yao Peng ◽

Gang Li ◽

Mei Zhou ◽

Huaile Wang ◽

Ling Lin

Keyword(s):

Wavelet Transform ◽

Independent Component Analysis ◽

Extraction Method ◽

Component Analysis ◽

Dynamic Spectrum ◽

Independent Component ◽

Blood Components ◽

Complex Wavelet Transform ◽

Non Invasive ◽

Invasive Measurement

The proposed new dynamic spectrum (DS) extraction method based on ICA combined DTCWT could improve the precision accuracy of non-invasive measurement of blood components effectively.

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Evaluation of dynamic spectrum extraction method based on salami slicing method

Infrared Physics & Technology ◽

10.1016/j.infrared.2020.103551 ◽

2020 ◽

Vol 111 ◽

pp. 103551

Author(s):

Qiuyue Xiao ◽

Gang Li ◽

Wenjuan Yan ◽

Guoquan He ◽

Ling Lin

Keyword(s):

Extraction Method ◽

Dynamic Spectrum ◽

Slicing Method

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Evaluation of the dark field method for large association of spread DNA

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100081814 ◽

1978 ◽

Vol 36 (2) ◽

pp. 190-191

Author(s):

Douglas C. Barker

Keyword(s):

Electron Microscopy ◽

Molecular Weight ◽

Mitochondrial Dna ◽

Extraction Method ◽

Field Method ◽

Dark Field ◽

Kinetoplast Dna ◽

Field Electron ◽

Field Electron Microscopy ◽

Dark Field Electron

A number of satisfactory methods are available for the electron microscopy of nicleic acids. These methods concentrated on fragments of nuclear, viral and mitochondrial DNA less than 50 megadaltons, on denaturation and heteroduplex mapping (Davies et al 1971) or on the interaction between proteins and DNA (Brack and Delain 1975). Less attention has been paid to the experimental criteria necessary for spreading and visualisation by dark field electron microscopy of large intact issociations of DNA. This communication will report on those criteria in relation to the ultrastructure of the (approx. 1 x 10-14g) DNA component of the kinetoplast from Trypanosomes. An extraction method has been developed to eliminate native endonucleases and nuclear contamination and to isolate the kinetoplast DNA (KDNA) as a compact network of high molecular weight. In collaboration with Dr. Ch. Brack (Basel [nstitute of Immunology), we studied the conditions necessary to prepare this KDNA Tor dark field electron microscopy using the microdrop spreading technique.

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A method for direct measurement of specimen noise in HREM images

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100148125 ◽

1993 ◽

Vol 51 ◽

pp. 458-459

Author(s):

Sidnei Paciornik ◽

Roar Kilaas ◽

Ulrich Dahmen ◽

Michael Adrian O'Keefe

Keyword(s):

Random Noise ◽

Image Interpretation ◽

Thin Foil ◽

High Resolution Electron Microscopy ◽

Black Dot ◽

Atomic Column ◽

Amorphous Oxide ◽

Resolution Electron ◽

Imaging Conditions ◽

Image Simulations

High resolution electron microscopy (HREM) is a primary tool for studying the atomic structure of defects in crystals. However, the quantitative analysis of defect structures is often seriously limited by specimen noise due to contamination or oxide layers on the surfaces of a thin foil.For simple monatomic structures such as fcc or bcc metals observed in directions where the crystal projects into well-separated atomic columns, HREM image interpretation is relatively simple: under weak phase object, Scherzer imaging conditions, each atomic column is imaged as a black dot. Variations in intensity and position of individual image dots can be due to variations in composition or location of atomic columns. Unfortunately, both types of variation may also arise from random noise superimposed on the periodic image due to an amorphous oxide or contamination film on the surfaces of the thin foil. For example, image simulations have shown that a layer of amorphous oxide (random noise) on the surfaces of a thin foil of perfect crystalline Si can lead to significant shifts in image intensities and centroid positions for individual atomic columns.

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