Degradation Estimation of 2.25CR-1Mo Steel by Barkhausen Noise Method

2004 ◽  
Author(s):  
Youn-Ho Cho ◽  
Ik-Keun Park
Keyword(s):  
Data Analysis ◽  
Thermal Degradation ◽  
Wavelet Analysis ◽  
Frequency Spectrum ◽  
Thermal Damage ◽  
Peak Amplitude ◽  
Barkhausen Noise ◽  
Average Amplitude ◽  
Damage Level ◽  
Maximum Peak

In this study, various NDE parameters of the Barkhausen noise method, such as MPA (Maximum Peak Amplitude), RMS, IABNS (Internal Area of Barkhausen Noise on Signal) and average amplitude of frequency spectrum are investigated and correlated with thermal damage level of 2.25Cr-1.0Mo steel using wavelet analysis. Those parameters tend to increase while thermal degradation proceeds. It also turns out that the wavelet technique can help to reduce experimental false calls in data analysis.

Characterization of the Thermal Degradation of Vinegar and the Construction of an Identification Model for Chinese Geographical Indication Vinegars by the Py-GC-MS Technique

2017 ◽  
Vol 100 (2) ◽  
pp. 503-509 ◽  
Author(s):  
Cen Xiong ◽  
Zhiyi Su ◽  
Yanjie Zhezng ◽  
Qi Wang ◽  
Yejing Ling ◽  
...  
Keyword(s):  
Data Analysis ◽  
Thermal Degradation ◽  
Least Squares ◽  
Partial Least Squares ◽  
Modeling Method ◽  
Geographical Indication ◽  
Time To Build ◽  
Pls Modeling ◽  
Identification Model

Abstract The pyrolysis (Py)-GC-MS technique was first introduced for the identification of two kinds of Chinese geographical indication vinegars because its advantages are that it is a simple and convenient sample pretreatmentand inlet method. Abundant Py information about vinegars was obtained using Py-GC-MS; 21 common peaks were selected. With the help of the classical partial least-squares (PLS) modeling method for data analysis, two identification models for Shanxi extra-aged (SX) and Zhenjiang (ZJ) vinegars were established, respectively. An N-reducing method was used to select the variables. The variables were reduced one at a time to build the PLS models with the lowest number of misjudgments. Both models had good recognition rates, identifying over 90% of samples correctly. Thus, combining Py-GC-MS and PLS could be regarded as an effective method for the identification of SX and ZJ vinegars.

Non-Stationary Harmonics Analysis Based on Wavelet Packet Transform and Neural Network

2014 ◽  
Vol 926-930 ◽  
pp. 1733-1737
Author(s):  
Wen Liang Zhao ◽  
Hong Song ◽  
Quan Pan ◽  
Ling Tang
Keyword(s):  
Neural Network ◽  
Wavelet Analysis ◽  
Frequency Spectrum ◽  
Wavelet Packet ◽  
Effective Means ◽  
Wavelet Packet Transform ◽  
Analysis Method ◽  
Software Simulation ◽  
Frequency Bands ◽  
Noise Interference

The method for analysis of stationary harmonics in power system is FFT, but it is unsuitable for non-stationary harmonics. Because of the feature that non-stationary harmonics’ frequency spectrum has a certain bandwidth and with some noise interference usually. A new method for detection, based on wavelet packet transform and neural network was presented in this paper. This method improved the traditional wavelet analysis method. The non-stationary harmonics were decomposed in different frequency bands by wavelet packet transform at first, and then complete the analysis of the non-stationary harmonic in different frequency bands. Through software simulation, the analysis results show that, the method has better accuracy, and provided an effective means for analyzing non-stationary harmonics.

Research of Denoising Technology about Wavelet Analysis with Wiener Filter

2013 ◽  
Vol 333-335 ◽  
pp. 597-600
Author(s):  
Yao Bin Hu ◽  
Liang Bin Hu ◽  
Qiang Cheng
Keyword(s):  
Signal Processing ◽  
Wavelet Analysis ◽  
White Noise ◽  
Frequency Spectrum ◽  
Wiener Filter ◽  
Power Line ◽  
Wiener Filtering ◽  
Power Line Communication ◽  
Denoising Method

Interfering noise of power line is one of the important factors which affects the quality of power line communication (PLC). Its frequency spectrum has the character of the 1/f process and the great autocorrelation. The wavelet analysis is an important signal-processing tool. Selecting suitable wavelet analysis can turn non-white noise to white noise, followed by wiener filtering, we can achieve the purpose of denoising. This paper introduces a denoising method of combining wavelet analysis with wiener filtering. Experiment proves this method has a strong feasibility and practical value.

Enhanced Inp Substrate Protection for Lpe Growth of Ingaasp DH Lasers

1982 ◽  
Vol 14 ◽  
Author(s):  
P. Besomi ◽  
R. B. Wilson ◽  
W. R. Wagner ◽  
R. J. Nelson
Keyword(s):  
Thermal Degradation ◽  
Partial Pressure ◽  
Single Crystal ◽  
Thermal Damage ◽  
X Ray ◽  
Inp Substrate ◽  
Substrate Protection ◽  
Inp Substrates

ABSTRACTThermal degradation of InP single crystal substrates prior to LPE growth has been virtually eliminated by using an improved protection technique. The phosphorus partial pressure provided by a Sn-In-P solution localized inside an external chamber surrounding the InP substrate prior to growth prevents thermal damage to the surface. Nomarski contrast photomicrographs,photoluminescence and X-ray diffractometric measurements indicate that InP substrates protected by this method suffer negligible deterioration, in contrast to the results of the more commonly used cover wafer method.

scholarly journals Evaluate Laser Needle Effect on Blood Perfusion Signals of Contralateral Hegu Acupoint with Wavelet Analysis

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Guangjun Wang ◽  
Yuying Tian ◽  
Shuyong Jia ◽  
Gerhard Litscher ◽  
Weibo Zhang
Keyword(s):  
Wavelet Analysis ◽  
Imaging System ◽  
Blood Perfusion ◽  
Original Data ◽  
Average Amplitude ◽  
Power Spectral ◽  
Myogenic Activity ◽  
Laser Needle ◽  
Stimulation Group ◽  
Acupoint Stimulation

Our previous studies suggested that the MBF in contralateral Hegu acupoint (IL4) increased after ipsilateral Hegu acupoint was stimulated with manual acupuncture. In this study, twenty-eight (28) healthy volunteers were recruited and were randomly divided into Hegu acupoint stimulation group and Non-Hegu stimulation group. All subjects received the same model stimulation of the laser needle for 30 min in right Hegu acupoint and Non-Hegu acupoint, respectively. MBF of left LI4 was measured by the laser Doppler perfusion imaging system. The original data dealt with morlet wavelet analysis and the average amplitude and power spectral density of different frequency intervals was acquired. The results indicated that right Hegu stimulation with the laser needle might result in the increase of left Hegu acupoint MBF. 40 min later after ceased stimulation, the MBF is still increasing significantly, whereas the MBF has no significantly change in Non-Hegu stimulation group. The wavelet analysis result suggested that compared to Non-Hegu stimulation, stimulated to right Hegu acupoint might result in the increase of average amplitude in frequency intervals of 0.0095–0.02 Hz, 0.02–0.06 Hz, and 0.06–0.15 Hz, which might be influenced by the endothelial, neurogenic, and the intrinsic myogenic activity of the vessel wall, respectively.

Frequency Spectrum of the Barkhausen Noise

1957 ◽  
Vol 28 (7) ◽  
pp. 777-780 ◽  
Author(s):  
G. Biorci ◽  
D. Pescetti
Keyword(s):  
Frequency Spectrum ◽  
Barkhausen Noise

scholarly journals Wavelet Analysis as a Tool for Studying the Road Traffic Characteristics in the Context of Intelligent Transport Systems with Incomplete Data

2019 ◽  
Vol 18 (2) ◽  
pp. 326-353
Author(s):  
Oleg Golovnin ◽  
Anastasia Stolbova
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Missing Data ◽  
Wavelet Analysis ◽  
Traffic Flow ◽  
Flow Characteristics ◽  
Traffic Monitoring ◽  
Sampling Interval ◽  
Intelligent Transport ◽  
Traffic Flow Characteristics

A frequent problem of traffic flow characteristics acquisition is data loss, which leads to uneven time series analysis. An effective approach to uneven data analysis is the spectral analysis, which requires obtaining process with a constant sampling interval, for example, by restoring missing data, which leads to the appearance of dating error. Thus, the main purpose of this study is to develop a method and software for wavelet analysis of traffic flow characteristics without restoring the missing data. To analyze and interpret non-stationary uneven time series obtained from traffic monitoring systems, we propose the wavelet transformation method with adjustment of the sampling intervals, which results in a time-frequency domain with a constant sampling interval. Wavelet analysis is applied to the macroscopic traffic flow characteristics. We developed the software for traffic flow wavelet analysis on the "ITSGIS" intelligent transport geo-information framework using the attribute-oriented approach. Wavelet analysis of traffic flows characteristics using Morlet wavelets was accomplished for data analysis of the city of Aarhus, Denmark. Wavelet spectra and scalograms were constructed and analyzed, general dependencies in the frequency distribution of extremes, and differences in spectral power were revealed. The developed software is being experimentally tested in solving practical problems of municipalities and road agencies in Russia.

Wavelet analysis and frequency spectrum of cloud cavitation around a sphere

2019 ◽  
Vol 182 ◽  
pp. 235-247 ◽  
Author(s):  
Arman Kolahan ◽  
Ehsan Roohi ◽  
Mohammad-Reza Pendar
Keyword(s):  
Wavelet Analysis ◽  
Frequency Spectrum ◽  
Cloud Cavitation
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