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%.

A Detection Method for Friction Vibration Based on Harmonic Wavelet Packet Transform and Cross-Correlation Analysis

2018 ◽  
Vol 140 (3) ◽  
Author(s):  
Pengfei Xing ◽  
Guobin Li ◽  
Ting Liu ◽  
Hongtao Gao ◽  
Guoyou Wang
Keyword(s):  
Steady State ◽  
Correlation Analysis ◽  
Cross Correlation ◽  
Detection Method ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Mean Square ◽  
Vibration Signals ◽  
Cross Correlation Analysis ◽  
Harmonic Wavelet

Running-in wear experiments were conducted on a spherical-on-disk tester. The vibration signals collected in the experiments were detected by a combination of harmonic wavelet packet transform (HWPT) and cross-correlation analysis (CCA) methods. Experimental results show that the friction vibration signals detected in tangential and normal directions have the characteristics of no time delay and strong correlation. Their root-mean-square (RMS) values gradually reduce and enter a steady-state of fluctuation with the experiments time, which are consistent with the variation of friction coefficient and reflect the change of wear states from the running-in wear to the stable wear. Therefore, the detection of friction vibration can be realized by a combination of HWPT and CCA methods.

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.

Evaluation of Diagnostic Performance for the Gear Box Considering the Measurement Position Effect With Wavelet Packet Transform

2009 ◽  
Author(s):  
Young-Sun Hong ◽  
Gil-Yong Lee ◽  
Young-Man Cho ◽  
Sung-Hoon Ahn ◽  
Chul-Ki Song
Keyword(s):  
Wavelet Packet ◽  
Position Effect ◽  
Vibration Signal ◽  
Industrial Applications ◽  
Wavelet Packet Transform ◽  
Bevel Gear ◽  
Diagnostic Methods ◽  
Vibration Signals ◽  
Rotary Machinery ◽  
Fault Features

There has been much research into monitoring techniques for mechanical systems to ensure stable production levels in modern industries. This is particularly true for the diagnostic monitoring of rotary machinery, because faults in this type of equipment appear frequently and quickly cause severe problems. Such diagnostic methods are often based on the analysis of vibration signals because they are directly related to physical faults. Even though the magnitude of vibration signals depends on the measurement position, the effect of measurement position is generally not considered. This paper describes an investigation of the effect of the measurement position on the fault features in vibration signals. The signals for normal and broken bevel gears were measured at the base, gearbox, and bevel gear, simultaneously, of a machine fault simulator (MFS). These vibration signals were compared to each other and used to estimate the classification efficiency of a diagnostic method using wavelet packet transform. From this experiment, the fault features are more prominently in the vibration signal from the measurement position of the bevel gear than from the base and gearbox. The results of this analysis will assist in selecting the appropriate measurement position in real industrial applications and precision diagnostics.

Energy Distribution Analysis Regarding the Crack Size in a Rotating Shaft

2019 ◽  
Vol 24 (3) ◽  
pp. 418-425
Author(s):  
Cristina Cristina Castejon ◽  
Marıa Jesus Gomez ◽  
Juan Carlos Garcia-Prada ◽  
Eduardo Corral
Keyword(s):  
Wavelet Transform ◽  
Crack Detection ◽  
Wavelet Packet ◽  
Critical Role ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Distribution Analysis ◽  
Rotating Shaft ◽  
Vibration Signals ◽  
Machine Monitoring

Maintenance is critical to avoid catastrophic failures in rotating machinery, and the detection of cracks plays a critical role because they can originate failures with costly processes of reparation, especially in shafts. Vibration signals are widely used in machine monitoring and fault diagnostics. The most critical issue in machine monitoring is the suitable selection of the vibration parameters that represent the condition of the machine. Discrete Wavelet Transform, and one of its recursive forms, called Wavelet Packet Transform, provide a high potential for pattern extraction. Several factors must be selected and taken into account in the Wavelet Transform application such as the level of decomposition, the suitable mother wavelet, and the level basis or features. In this work, the dynamic response of a shaft with different levels of crack is studied. The evolution of energy of the vibration signals obtained from the rotating shaft and the frequencies where maximum increments of energy appear with the crack are analyzed. The results allow the conclusion that changes in energies computed by means of the Wavelet Packet Transform can be successfully used for crack detection.

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.

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.

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.

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