Wavelet Cross-Correlation Analysis of the Unsteady Signals of the Flow in the Open Sump

Volume 1 ◽  
2004 ◽  
Author(s):  
Mansa Kante ◽  
Yulin Wu ◽  
Yong Li ◽  
Shuhong Liu ◽  
Daqing Zhou
Keyword(s):  
Cross Correlation ◽  
Correlation Method ◽  
Vibration Signal ◽  
Structural Similarity ◽  
Signal Distribution ◽  
Cross Correlation Analysis ◽  
Correlation Scale ◽  
Pump Sump ◽  
The Relationship ◽  
Signal Analysis Method

The wavelet cross-correlation method was used to analyze the unsteady signals of the flow of the model open pump sump, which include pressure signal, vibration signal and acoustic signal. The continuous wavelet transform was done first to find the signal distribution at various periods and at any time, then the wavelet cross-correlation was used to find the relationship between the signals taken two a two. Through comparing the result of wavelet cross-correlation and the result of classic cross-correlation, one can find the correlation scale of any two unsteady signals (pressure-vibration, pressure-noise, and vibration-noise). The signal on the correlation scale was reconstruct and its characteristics were obtained using classical signal analysis method same as the structural similarity of a arbitrary two signals.

scholarly journals Feature Analysis of Degradation Signal of Rolling Stock Lateral Damper

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 298
Author(s):  
Zhiyan Zhao ◽  
Bin Wu ◽  
Ting Zhou
Keyword(s):  
Cross Correlation ◽  
Wavelet Packet ◽  
Reference Signal ◽  
Correlation Method ◽  
Health Condition ◽  
Wavelet Packet Transform ◽  
Rolling Stock ◽  
Health State ◽  
Vibration Signals ◽  
Cross Correlation Analysis

The lateral damper is one of the key components of rolling stock. Establishing the relationship between the degraded signal and the health state of the lateral damper is important in order to perform timely performance detection and fault diagnosis. This paper proposes a wavelet packet cross-correlation method (WPCC) that is based on wavelet packet transform (WPT) and cross-correlation analysis (CCA). First, the vibration signals under different running speeds, different running conditions, and different track excitations were collected and analyzed. Second, the wavelet packet transform was used to select larger energy band signals for reconstruction. Subsequently, the WPCC coefficient was calculated between the reference signal and the signal to be measured. The proposed method was applied to analysis of vibration signals of the lateral damper performance degradation. The lateral damper health condition was divided into four intervals, and the average accuracy calculated under different running speeds, different running conditions, and different track excitation was 95%.

scholarly journals Fluctuation Characteristics of Water Level and Water Temperature of Huize Well Based on MF-DCCA

2021 ◽  
Vol 39 (3) ◽  
pp. 825-832
Author(s):  
Jian Yu ◽  
Lili Sui ◽  
Yirong Xu ◽  
Baoming Chi
Keyword(s):  
Time Series ◽  
Water Temperature ◽  
Water Level ◽  
Cross Correlation ◽  
Correlation Method ◽  
Multifractal Spectrum ◽  
Earthquake Precursor ◽  
Observation Data ◽  
Cross Correlation Analysis ◽  
Abnormal Increase

In recent decades, the network of seismic subsurface fluid observatories is developing constantly, the observation data of subsurface fluids are enriched accordingly, which provides a favorable condition for the research on the formation, occurrence, and development of earthquakes. In the observation data of subsurface fluids, water level and water temperature changes are very important observation indicators, and their fluctuation sequences are quite complicated. Therefore, this paper employed a non-linear cross-correlation method to study the relationship between the water level and water temperature of Huize Well from 2004 to 2006, and found that there’s a significant cross-correlation between the time series of water level and water temperature; then, this study adopted DCCA (detrended cross-correlation analysis) to calculate the cross-correlation coefficient under different scales and explore the continuous changes of water level and water temperature; at last, this paper used the MF-DCCA (Multifractal-DCCA) method to prove that there’s multifractal cross-correlation between the time series of water level and water temperature. Before the M5.1 earthquake in Huize area, there’s an abnormal increase in the width of the multifractal spectrum of the water level and water temperature drawn with a sliding window of 500-hour, and this is a possible earthquake precursor.

The distribution of corticotectal projection neurons correlates with the interblob compartment in macaque striate cortex

1996 ◽  
Vol 13 (3) ◽  
pp. 461-466 ◽  
Author(s):  
Barry Lia ◽  
Jaime F. Olavarria
Keyword(s):  
Cross Correlation ◽  
Random Distribution ◽  
Striate Cortex ◽  
Projection Neurons ◽  
Chi Square ◽  
Cortical Projections ◽  
Cross Correlation Analysis ◽  
Chi Square Analysis ◽  
Relationship Of ◽  
The Relationship

AbstractWhile much attention has been given to the correlation between cytochrome-oxidase (CO) compartments and patterns of cortico-cortical projections originating from supragranular layers in the striate cortex, little is known in this regard about patterns of cortico-subcortical projections originating from infragranular cortex. We studied the tangential distribution of the striate cortex neurons projecting to the superior colliculus and used two approaches to analyze the relationship of this distribution to the arrangement of CO “blobs.” First, chi-square analysis indicated that significantly fewer labeled neurons were found within the CO blob compartment than the number expected for a random distribution. Second, spatial cross-correlation analysis – which circumvents the inherent subjectivity of delineating blob boundaries – revealed an area around blob centers in which there was a decreased probability of encountering labeled cells. The size of this area compared well with that of our outlines of CO blobs. We conclude that corticotectal projection neurons in the striate cortex are distributed preferentially within the interblob compartment of the infragranular striate cortex. These results demonstrate that the spatial distribution of cortico-subcortical projection neurons within infragranular cortex can correlate with the CO architecture of the primary visual cortex.

scholarly journals A qualitative study of the seismo-ionospheric precursors prior to the 6 April 2009 earthquake in L'Aquila, Italy

2010 ◽  
Vol 10 (1) ◽  
pp. 133-137 ◽  
Author(s):  
G. S. Tsolis ◽  
T. D. Xenos
Keyword(s):  
Correlation Coefficient ◽  
Cross Correlation ◽  
Seismic Event ◽  
Correlation Method ◽  
L’Aquila Earthquake ◽  
Earthquake Preparation ◽  
Mode Decomposition ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method ◽  
The Cross

Abstract. In this paper we use the Cross Correlation analysis method in conjunction with the Empirical Mode Decomposition to analyze foF2 signals collected from Rome, Athens and San Vito ionospheric stations, in order to verify the existence of seismo-ionospheric precursors prior to M=6.3 L'Aquila earthquake in Italy. The adaptive nature of EMD allows for removing the geophysical noise from the foF2 signals, and then to calculate the correlation coefficient between them. According to the cross correlation coefficient theory, we expect the stations which located inside the earthquake preparation area, as evaluated using Dobrovolsky equation, to capture the ionospheric disturbances generated by the seismic event. On the other hand the stations outside of this area are expected to remain unaffected. The results of our study are in accordance with the theoretical model, evidencing ionospheric modification prior to L'Aquila earthquake in a certain area around the epicenter. However, it was found that the selection of stations at the limits of the theoretically estimated earthquake preparation area is not the best choice when the cross correlation method is applied, since the modification of the ionosphere over these stations may not be enough for the ionospheric precursors to appear. Our experimental results also show that when a seismic event constitutes the main shock after a series of pre-seismic activity, precursors may appear as early as 22 days prior to the event.

scholarly journals Shaft Crack Identification Based on Vibration and AE Signals

2011 ◽  
Vol 18 (1-2) ◽  
pp. 115-126 ◽  
Author(s):  
Wenxiu Lu ◽  
Fulei Chu
Keyword(s):  
Cross Correlation ◽  
Crack Depth ◽  
Correlation Method ◽  
Vibration Signal ◽  
Vibration Response ◽  
Crack Identification ◽  
The Finite Element Method ◽  
Cracked Rotor ◽  
Ae Signal ◽  
Ae Signals

The shaft crack is one of the main serious malfunctions that often occur in rotating machinery. However, it is difficult to locate the crack and determine the depth of the crack. In this paper, the acoustic emission (AE) signal and vibration response are used to diagnose the crack. The wavelet transform is applied to AE signal to decompose into a series of time-domain signals, each of which covers a specific octave frequency band. Then an improved union method based on threshold and cross-correlation method is applied to detect the location of the shaft crack. The finite element method is used to build the model of the cracked rotor, and the crack depth is identified by comparing the vibration response of experiment and simulation. The experimental results show that the AE signal is effective and convenient to locate the shaft crack, and the vibration signal is feasible to determine the depth of shaft crack.

Phase Correlation Processing for DPIV Measurements: Part I — Spatial Domain Analysis

2007 ◽  
Author(s):  
Adric C. Eckstein ◽  
John J. Charonko ◽  
Pavlos P. Vlachos
Keyword(s):  
Nonlinear Filtering ◽  
Cross Correlation ◽  
Correlation Method ◽  
Spatial Filter ◽  
Phase Correlation ◽  
Image Correlation ◽  
Superior Performance ◽  
Spatial Filters ◽  
Cross Correlation Analysis ◽  
Improve Measurement

A novel Digital Particle Image Velocimetry (DPIV) correlation method is presented using nonlinear filtering techniques coupled with a phase-only filter (POF) generalized cross-correlation (GCC). Error analysis demonstrates the deficiency of the GCC alone to successfully improve measurement accuracy, as shown from a decrease in valid vector detection as well as an increase in bias and RMS errors. The use of spatial filters is commonly argued to be disadvantageous for standard Fourier based cross-correlation analysis [1]. However, here we demonstrate that appropriate spatial filters applied to the GCC provide vastly superior performance. The zeropadding method as well as a new spatial filter derived from the Blackman window is introduced, both of which are able to enhance the vector detection capabilities of the GCC processor. In addition, a Gaussian transform filtering technique is introduced which dramatically increases the subpixel resolution of the GCC processor, alleviating apparent effects of peak-locking. The use of these filters with the GCC is able to provide striking improvements in DPIV image correlation.

scholarly journals Application of Vibration Signal Processing Methods to Detect and Diagnose Wheel Flats in Railway Vehicles

2021 ◽  
Vol 11 (5) ◽  
pp. 2151
Author(s):  
JaeSeok Shim ◽  
GeoYoung Kim ◽  
ByungJin Cho ◽  
JeongSeo Koo
Keyword(s):  
Signal Processing ◽  
Cross Correlation ◽  
Vibration Signal ◽  
Analysis Method ◽  
Order Analysis ◽  
Cepstrum Analysis ◽  
Cross Correlation Analysis ◽  
Train Speed ◽  
Wheel Flat ◽  
Signal Processing Methods

This paper studied two useful vibration signal processing methods for detection and diagnosis of wheel flats. First, the cepstrum analysis method combined with order analysis was applied to the vibration signal to detect periodic responses in the spectrum for a rotating body such as a wheel. In the case of railway vehicles, changes in speed occur while driving. Thus, it is difficult to effectively evaluate the flat signal of the wheel because the time cycle of the flat signal changes frequently. Thus, the order analysis was combined with the existing cepstrum analysis method to consider the changes in train speed. The order analysis changes the domain of the vibration signal from time domain to rotating angular domain to consider the train speed change in the cepstrum analysis. Second, the cross correlation analysis method combined with the order analysis was applied to evaluate the flat signal from the vibration signal well containing the severe field noise produced by the vibrations of the rail irregularities and bogie components. Unlike the cepstrum analysis method, it can find out the wheel flat size because the flat signal linearly increases to the wheel flat. Thus, it is more effective when checking the size of the wheel flat. Finally, the data tested in the Korea Railroad Research Institute were used to confirm that the cepstrum analysis and cross correlation analysis methods are appropriate for not only simulation but also test data.

Asymmetric MF-DCCA Method Based on Fluctuation Conduction and its Application in Air Pollution in Hangzhou

2019 ◽  
Vol 23 (5) ◽  
pp. 823-830
Author(s):  
Chaohui Xiang ◽  
◽  
Xiaozhen Hao ◽  
Wenhui Wang ◽  
Zhenlong Chen
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Cross Correlation ◽  
Conduction Mechanism ◽  
Urban Air ◽  
Conduction Time ◽  
Quality Factors ◽  
Cross Correlation Analysis ◽  
Fractal Fluctuation ◽  
The Relationship

The study of the relationship between the concentration of PM2.5 and the local air quality index (AQI) is significant for the improvement of urban air quality. This study not only considered multifractal cross-correlation but also the fluctuation conduction mechanism. An asymmetric multifractal detrended cross-correlation analysis (MF-DCCA) method based on fluctuation conduction is introduced here to empirically explore the causality and conduction time between air quality factors and PM2.5 concentration. The empirical results indicate the existence of a bidirectional fluctuation conduction effect between PM2.5 and PM10, SO2, and NO2 in Hangzhou, China, with a conduction time of 30 hours; this effect is non-existent between PM2.5 and O3. In addition, there is a unidirectional fractal fluctuation conduction between PM2.5 and CO with a conduction time of 21 hours.

scholarly journals Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2529 ◽  
Author(s):  
Shanshan Tian ◽  
Mengxuan Li ◽  
Yifei Wang ◽  
Xi Chen
Keyword(s):  
Correlation Analysis ◽  
Correlation Coefficient ◽  
Cross Correlation ◽  
Correlation Method ◽  
Disease Diagnosis ◽  
Characteristic Analysis ◽  
Cross Correlation Analysis ◽  
Gait Characteristic ◽  
Scaling Index ◽  
Coefficient Method

Hemiparesis is one of the common sequelae of neurological diseases such as strokes, which can significantly change the gait behavior of patients and restrict their activities in daily life. The results of gait characteristic analysis can provide a reference for disease diagnosis and rehabilitation; however, gait correlation as a gait characteristic is less utilized currently. In this study, a new non-contact electrostatic field sensing method was used to obtain the electrostatic gait signals of hemiplegic patients and healthy control subjects, and an improved Detrended Cross-Correlation Analysis cross-correlation coefficient method was proposed to analyze the obtained electrostatic gait signals. The results show that the improved method can better obtain the dynamic changes of the scaling index under the multi-scale structure, which makes up for the shortcomings of the traditional Detrended Cross-Correlation Analysis cross-correlation coefficient method when calculating the electrostatic gait signal of the same kind of subjects, such as random and incomplete similarity in the trend of the scaling index spectrum change. At the same time, it can effectively quantify the correlation of electrostatic gait signals in subjects. The proposed method has the potential to be a powerful tool for extracting the gait correlation features and identifying the electrostatic gait of hemiplegic patients.

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