The Research of the Spectral Features of Vibration Signals from Underground Railway Based on Wavelet Transform

2011 ◽  
Vol 243-249 ◽  
pp. 3463-3467
Author(s):  
Zhi Gang Yin
Keyword(s):  
Wavelet Transform ◽  
Signal Analysis ◽  
Spectral Features ◽  
Maximum Acceleration ◽  
Vibration Signals ◽  
Matlab Program ◽  
Underground Railway ◽  
Non Stationary Signal ◽  
Frequency Properties ◽  
Acceleration Signals

Using wavelet transform, signal’s frequency properties of vibration induced by underground are analyzed A MATLAB program is developed in order to decompose and reconstruct acceleration signals. The law that acceleration signals change in time-spectral domain is got. Then the relations of vibration signal’s maximum acceleration, energy, frequency and spectral are discussed. In contrast to conventional Fourier Transform， wavelet analysis can provide the evolution of spectral features of a signal as this evolves in time. It is ideal for random and non-stationary signal analysis.

Application of Harmonic Wavelet Analysis to Rubbing Vibration Signals for Rotating Machinery Fault Diagnosis

2013 ◽  
Vol 321-324 ◽  
pp. 1245-1248
Author(s):  
Xiang Wang ◽  
Yuan Zheng
Keyword(s):  
Wavelet Transform ◽  
Signal Analysis ◽  
Rotating Machinery ◽  
The Other ◽  
Vibration Signals ◽  
Time Frequency ◽  
Vibration Data ◽  
Turbo Generator ◽  
Harmonic Wavelet ◽  
Machinery Fault Diagnosis

Harmonic wavelet transform (HWT)and harmonic wavelet time-frequency profile plot (TFPP) is introduced firstly in practice to identify weak singularity in a signal with noise clearly. With TFPP method, emulational signal and vibration data of the rubbing of the large practical turbo-generator units are analyzed successfully, which prove that the method is effectively extract the rubbing signal feature which is can not gained by the other signal analysis methods, and the rubbing of the turbo-generator units is identified effectively.

scholarly journals THE USE OF A WAVELET TRANSFORMATION FOR REMOVAL OF THE NOISE COMPONENT FROM THE VIBRO SIGNAL

2021 ◽  
pp. 106-111
Author(s):  
А. Передерко
Keyword(s):  
Wavelet Transform ◽  
Signal Analysis ◽  
Wavelet Transformation ◽  
Vibration Signal ◽  
Distribution Law ◽  
Noise Component ◽  
Vibration Signals ◽  
The Past ◽  
Principle Of Causality ◽  
Filtering Problem

The article investigates the use of wavelets to remove noise from the measuring vibration signal. It is determined that wavelets are well adapted for signal analysis, for which the principle of causality is important: wavelets preserve the direction of time and do not create parasitic interference between the past and the future. Criteria for selecting an analytical wavelet have been developed, depending on what information should be extracted from the signal and the need to more fully identify and emphasize certain properties of the analyzed signal. It is proposed to use Daubechies wavelets to process the vibration signal data. The simulation of vibration signal filtering from noise with the normal distribution law is performed in the MATCAD package. It is proved that the method of wavelet transform allows to solve the problem of filtering the vibration signal from noise when processing vibration signals obtained by autonomous recording devices in conditions of increased interference from the environment. The obtained results evidence to the prospects of the developed method and its advantages in comparison with the hardware solution of the filtering problem.

scholarly journals A High-Resolution Dyadic Transform for Non-Stationary Signal Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3041
Author(s):  
Eduardo Trutié-Carrero ◽  
Diego Seuret-Jimenez ◽  
José M. Nieto-Jalil
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Dyadic Wavelet ◽  
Dyadic Wavelet Transform ◽  
Noise Ratio ◽  
The Fourier Transform ◽  
Non Stationary Signal

This article shows a new Te-transform and its periodogram for applications that mainly exhibit stochastic behavior with a signal-to-noise ratio lower than −30 dB. The Te-transform is a dyadic transform that combines the properties of the dyadic Wavelet transform and the Fourier transform. This paper also provides another contribution, a corollary on the energy relationship between the untransformed signal and the transformed one using the Te-transform. This transform is compared with other methods used for the analysis in the frequency domain, reported in literature. To perform the validation, the authors created two synthetic scenarios: a noise-free signal scenario and another signal scenario with a signal-to-noise ratio equal to −69 dB. The results show that the Te-transform improves the sensitivity in the frequency spectrum with respect to previously reported methods.

An Advanced Strategy for Detecting Impulses in Mechanical Signals

2005 ◽  
Vol 127 (3) ◽  
pp. 280-284 ◽  
Author(s):  
Wen-xian Yang ◽  
Peter W. Tse
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Spectral Features ◽  
Continuous Wavelet ◽  
Exponential Functions ◽  
Soft Thresholding ◽  
Thresholding Method ◽  
Mechanical Signals ◽  
Soft Threshold

The appearance of overlapping in the results derived by continuous wavelet transform (CWT) smears the spectral features and makes the results difficult to interpret. This will significantly affect the accuracy of analysis of anomalous signals. Aiming at minimizing the undesired effect of overlapping, a new soft-thresholding method in terms of exponential functions is proposed. Using the proposed soft-threshold and combining with Donoho’s approach for reducing the structures induced by noise, a strategy for purifying the results derived by the CWT is designed. A series of simulated and practical experiments show that, after adopting the proposed strategy, the results of CWT are further purified and thereby the spectral features of the inspected signal become more explicit and much more easily identified.

scholarly journals Energy Analysis Method Based on Wavelet Transform for Sensor Fault Diagnosis

2011 ◽  
Vol 2-3 ◽  
pp. 117-122 ◽  
Author(s):  
Peng Peng Qian ◽  
Jin Guo Liu ◽  
Wei Zhang ◽  
Ying Zi Wei
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Energy Analysis ◽  
Sensor Faults ◽  
Analysis Method ◽  
Sensor Fault ◽  
Fault Type ◽  
Ideal Tool ◽  
Non Stationary Signal

Wavelet analysis with its unique features is very suitable for analyzing non-stationary signal, and it can also be used as an ideal tool for signal processing in fault diagnosis. The characteristics of the faults and the necessary information on the diagnosis can be constructed and extracted respectively by wavelet analysis. Though wavelet analysis is specialized in characteristics extraction, it can not determine the fault type. So this paper has proposed an energy analysis method based on wavelet transform. Experiment results show the method is very effective for sensor fault diagnosis, because it can not only detect the sensor faults, but also determine the fault type.

Characterization of Cavitation During Pump Operation Based on Hilbert-Huang Transform

2018 ◽  
Author(s):  
Hui Sun ◽  
Shouqi Yuan ◽  
Yin Luo ◽  
Bo Gong
Keyword(s):  
Centrifugal Pump ◽  
Longitudinal Vibration ◽  
Process Time ◽  
Current Signal ◽  
Vibration Signals ◽  
Pump Operation ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Cavitation Process ◽  
Non Stationary Signal

Cavitation has negative influence on pump operation. In order to detect incipient cavitation effectively, experimental investigation was conducted to through acquisition of current and vibration signals during cavitation process. In this research, a centrifugal pump was modeled for research. The data was analyzed by HHT method. The results show that Torque oscillation resulted from unsteady flow during cavitation process could result in energy variation. Variation regulation of RMS of IMF in current signal is similar to that in axial vibration signal. But RMS of IMF in current signal is more sensitive to cavitation generation. It could be regarded as the indicator of incipient cavitation. RMS variation of IMF in base, radial, longitudinal vibration signals experiences an obvious increasing when cavitation gets severe. Such single variation regulation could be selected as the indicator of cavitation stage recognition. Hilbert-Huang transform is suitable for transient and non-stationary signal process. Time-frequency characteristics could be extracted from results of HHT process to reveal pump operation condition. The contents of current work could provide valuable references for further research on centrifugal pump operation detection.

Vibration Analysis of Gears with Fatigue Crack in Tooth Root

2006 ◽  
Vol 324-325 ◽  
pp. 835-838
Author(s):  
Aleš Belšak ◽  
Jože Flašker
Keyword(s):  
Fatigue Crack ◽  
Laboratory Test ◽  
Signal Analysis ◽  
Vibration Analysis ◽  
Operating Conditions ◽  
Test Plant ◽  
Tooth Root ◽  
Unit Operation ◽  
Time Frequency ◽  
Non Stationary Signal

A crack in the tooth root, which often leads to failure in gear unit operation, is the most undesirable damage caused to gear units. This article deals with fault analyses of gear units with real damages. Numerical simulations of real operating conditions have been used in relation to the formation of those damages. A laboratory test plant has been used and a possible damage can be identified by monitoring vibrations. The influences of defects of a single-stage gear unit upon the vibrations they produce are presented. Signal analysis has been performed also in concern to a non-stationary signal, using the Time Frequency Analysis tools. Typical spectrograms, which are the result of reactions to damages, are a very reliable indication of the presence of damages.

scholarly journals Метод трансляционного переноса для оценки стабильности нестационарного квантового стандарта частоты

2021 ◽  
Vol 91 (1) ◽  
pp. 32
Author(s):  
С.В. Божокин ◽  
К.А. Баранцев ◽  
А.Н. Литвинов
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Frequency Standard ◽  
Continuous Wavelet ◽  
Quantum Frequency ◽  
Quantum Frequency Standard ◽  
Standard Signal ◽  
Wavelet Variance ◽  
Non Stationary Signal

Continuous wavelet transform is used to analyze the operation of a non-stationary signal of a quantum frequency standard. The method of translational transfer is proposed, with the help of which the boundary phenomena in this transformation are eliminated. The spectral integrals of the quantum frequency standard signal in various frequency ranges are calculated. A wavelet dispersion is introduced, which makes it possible to determine the moments of time when the signal fluctuations are the strongest. The comparison of the wavelet variance with the usual variance and with the Allen variance is carried out.

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