Research on Coupling Faults With Oil-Film Instability and Bearing Rub-Impact Based on Reassigned Wavelet Scalogram

2007 ◽  
Author(s):  
Xiaopeng Li ◽  
Hui Ma ◽  
Guiqiu Song ◽  
Bangchun Wen
Keyword(s):  
Low Frequency ◽  
Decisive Role ◽  
Frequency Resolution ◽  
Vibration Signals ◽  
Oil Film ◽  
Rotor Systems ◽  
Time Frequency ◽  
Coupling Faults ◽  
Film Instability ◽  
Frequency Components

An experiment rig was set up to simulate coupling faults with oil-film instability and local rub-impact. The collected vibration signals were roughly analyzed by 3-D waterfall spectra. In complicated frequency components domain, the vibration signals were analyzed with wavelet scalogram and reassigned wavelet scalogram. Results show that reassigned wavelet scalogram has higher time-frequency resolution than wavelet scalogram and can well identify such close low-frequency components. The analysis results also indicated rub-impact faults induced by oil-film instability can produce some low-frequency components with small amplitude and oil whip can produce half frequency component; rub-impact with a common extent exerts a light influence on rotor systems and nonlinear oil-film force plays a decisive role in rotor systems.

scholarly journals Multi-scale time-frequency domain full waveform inversion with a weighted local correlation-phase misfit function

2019 ◽  
Vol 16 (6) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yong Hu ◽  
Liguo Han ◽  
Rushan Wu ◽  
Yongzhong Xu
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Local Correlation ◽  
Time Frequency ◽  
Model Dependence ◽  
Full Waveform ◽  
Frequency Components

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.

MECHANICAL WATCH SIGNATURE ANALYSIS BASED ON WAVELET DECOMPOSITION

2009 ◽  
Vol 07 (04) ◽  
pp. 491-512 ◽  
Author(s):  
QINGBO HE ◽  
RUXU DU
Keyword(s):  
Acoustic Signal ◽  
Wavelet Decomposition ◽  
Wavelet Packet ◽  
Wide Frequency Range ◽  
Frequency Resolution ◽  
Signature Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Wavelet Techniques

The acoustic signal of mechanical watch is a distinct multi-component signal. It contains many frequency components corresponding to specific escapement motion sources with a very wide frequency range. Therefore, it is challenging for signature analysis of mechanical watch by the acoustic signal. This paper studies the time-frequency signatures of the mechanical watch based on wavelet decomposition. Two methods are proposed to improve the frequency resolution of traditional wavelet techniques by combining other beneficial techniques in the sense of decomposing specific mono- or independent components. The empirical mode decomposition (EMD) is presented to advance the wavelet packet decomposition (WPD) to decompose the mono-component signals. And the blind source separation (BSS) makes the redundancy of continuous wavelet transform (CWT) further gain good frequency resolution in the independent meaning. The decomposed signals by the two methods reveal the insight of mechanical watch movement and can contribute much simpler and clearer time-frequency signatures. Experimental results indicated the effectiveness of the two methods and the value of the time-frequency signatures in analyzing and diagnosing mechanical watch movements.

A Method for Extracting Characteristic Frequency Components of Blood Flow Signals Based on Wavelet Transform

2013 ◽  
Vol 313-314 ◽  
pp. 1221-1224 ◽  
Author(s):  
Ruo Fei Cui ◽  
Si Te Luo ◽  
Li Qian Lu ◽  
Wei Wei Zhou ◽  
Zeng Yong Li
Keyword(s):  
Blood Flow ◽  
Wavelet Transform ◽  
Characteristic Frequency ◽  
Frequency Resolution ◽  
Peripheral Blood Flow ◽  
Frequency Interval ◽  
Frequency Method ◽  
Doppler Flowmetry ◽  
Time Frequency ◽  
Frequency Components

The objective of this paper is to propose a method for exacting the characteristic frequency components of blood flow signals based on wavelet transform. The wavelet transform technique, a time-frequency method with logarithmic frequency resolution, was used to analyze oscillations in human peripheral blood flow measured by laser Doppler flowmetry (LDF). In the frequency interval from 0.008 to 2.0 Hz, the LDF signal consists of components with five different characteristic frequenciesmetabolic (0.008-0.02Hz), neurogenic (0.02-0.06Hz), myogenic (0.06-0.15Hz), respiratory (0.15-0.4Hz) and cardiac (0.4-2.0Hz). The five frequency components were extracted in time domain and reconstructed using cubic spline interpolation in this study. The results showed that it was an effective way to extract each component of blood flow signals.

scholarly journals Fault Detection Enhancement in Rolling Element Bearings via Peak-Based Multiscale Decomposition and Envelope Demodulation

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Hua-Qing Wang ◽  
Wei Hou ◽  
Gang Tang ◽  
Hong-Fang Yuan ◽  
Qing-Liang Zhao ◽  
...  
Keyword(s):  
Fault Detection ◽  
Low Frequency ◽  
Frequency Noise ◽  
Rolling Element Bearings ◽  
Wavelet Domain ◽  
Vibration Signals ◽  
Multiscale Decomposition ◽  
Rolling Element ◽  
Frequency Components ◽  
Noise Threshold

Vibration signals of rolling element bearings faults are usually immersed in background noise, which makes it difficult to detect the faults. Wavelet-based methods being used commonly can reduce some types of noise, but there is still plenty of room for improvement due to the insufficient sparseness of vibration signals in wavelet domain. In this work, in order to eliminate noise and enhance the weak fault detection, a new kind of peak-based approach combined with multiscale decomposition and envelope demodulation is developed. First, to preserve effective middle-low frequency signals while making high frequency noise more significant, a peak-based piecewise recombination is utilized to convert middle frequency components into low frequency ones. The newly generated signal becomes so smoother that it will have a sparser representation in wavelet domain. Then a noise threshold is applied after wavelet multiscale decomposition, followed by inverse wavelet transform and backward peak-based piecewise transform. Finally, the amplitude of fault characteristic frequency is enhanced by means of envelope demodulation. The effectiveness of the proposed method is validated by rolling bearings faults experiments. Compared with traditional wavelet-based analysis, experimental results show that fault features can be enhanced significantly and detected easily by the proposed method.

Development of Spike Wavelet Analysis and Its Application to Damage Analysis on Gearbox

2006 ◽  
Vol 321-323 ◽  
pp. 1233-1236
Author(s):  
Sang Kwon Lee ◽  
Jang Sun Sim
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Gear System ◽  
Frequency Resolution ◽  
Linear Scaling ◽  
Continuous Wavelet ◽  
Vibration Signals ◽  
Gear Fault

Impulsive sound and vibration signals in gear system are often associated with their faults. Thus these impulsive sound and vibration signals can be used as indicators in condition monitoring of gear system. The traditional continuous wavelet transform has been used for detection of impulsive signals. However, it is often difficult for the continuous wavelet transform to identify spikes at high frequency and meshing frequencies at low frequency simultaneously since the continuous wavelet transform is to apply the linear scaling (a-dilation) to the mother wavelet. In this paper, the spike wavelet transform is developed to extract these impulsive sound and vibration signals. Since the spike wavelet transform is to apply the non-linear scaling, it has better time resolution at high frequency and frequency resolution at low frequency than that of the continuous wavelet transform respectively. The spike wavelet transform can be, therefore, used to detect fault position clearly without the loss of information for the damage of a gear system. The spike wavelet transform is successfully is applied to detection of the gear fault with tip breakage.

Localization of Lamb Wave Scattering Source Based on Time-Frequency Analysis

2013 ◽  
Vol 281 ◽  
pp. 276-281
Author(s):  
Ding Ma ◽  
Li Hua Shi ◽  
Shang Chen Fu ◽  
Hong Fu Cao
Keyword(s):  
Lamb Wave ◽  
Low Frequency ◽  
Wave Dispersion ◽  
Frequency Component ◽  
Localization Method ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Scattering Signal ◽  
Frequency Curves

Considering the influence of Lamb wave dispersion on the precision of damage detection, a new detection method of scattering source based on time-frequency curves and ellipse localization method is proposed. Empirical mode decomposition(EMD) is used to decompose the scattering signal into finite narrowband signals, and a modified continuous wavelet transform(CWT) is further used to get the time-frequency distribution(TFD) of the detected signal, and the arriving time of different frequency component is estimated based on TFD. A series of location results can be obtained from different frequency components using ellipse localization method. The damage position can finally be estimated by synthesizing localization results at different frequencies. Experiments on aluminum plate are conducted to demonstrate the efficiency of the proposed method. EMD-CWT analysis can get precise time-frequency curves in highly dispersive low frequency band of A0 mode. The damage location results is more accurate and the influence from occasional factors can be suppressed by using the synthesized method.

Seismic spectral decomposition using deconvolutive short-time Fourier transform spectrogram

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. V43-V51 ◽  
Author(s):  
Wenkai Lu ◽  
Fangyu Li
Keyword(s):  
Fourier Transform ◽  
Spectral Decomposition ◽  
Reservoir Characterization ◽  
Low Frequency ◽  
Seismic Signal ◽  
Frequency Resolution ◽  
Short Time Fourier Transform ◽  
Distribution Method ◽  
Time Frequency ◽  
Short Time

The spectral decomposition technique plays an important role in reservoir characterization, for which the time-frequency distribution method is essential. The deconvolutive short-time Fourier transform (DSTFT) method achieves a superior time-frequency resolution by applying a 2D deconvolution operation on the short-time Fourier transform (STFT) spectrogram. For seismic spectral decomposition, to reduce the computation burden caused by the 2D deconvolution operation in the DSTFT, the 2D STFT spectrogram is cropped into a smaller area, which includes the positive frequencies fallen in the seismic signal bandwidth only. In general, because the low-frequency components of a seismic signal are dominant, the removal of the negative frequencies may introduce a sharp edge at the zero frequency, which would produce artifacts in the DSTFT spectrogram. To avoid this problem, we used the analytic signal, which is obtained by applying the Hilbert transform on the original real seismic signal, to calculate the STFT spectrogram in our method. Synthetic and real seismic data examples were evaluated to demonstrate the performance of the proposed method.

scholarly journals Application of Reassigned Wavelet Scalogram in Wind Turbine Planetary Gearbox Fault Diagnosis under Nonstationary Conditions

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaowang Chen ◽  
Zhipeng Feng
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Frequency Resolution ◽  
Vibration Signals ◽  
Cross Term ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Gear Fault ◽  
Nonstationary Conditions

Wind turbine planetary gearboxes often run under nonstationary conditions due to volatile wind conditions, thus resulting in nonstationary vibration signals. Time-frequency analysis gives insight into the structure of an arbitrary nonstationary signal in joint time-frequency domain, but conventional time-frequency representations suffer from either time-frequency smearing or cross-term interferences. Reassigned wavelet scalogram has merits of fine time-frequency resolution and cross-term free nature but has very limited applications in machinery fault diagnosis. In this paper, we use reassigned wavelet scalogram to extract fault feature from wind turbine planetary gearbox vibration signals. Both experimental and in situ vibration signals are used to evaluate the effectiveness of reassigned wavelet scalogram in fault diagnosis of wind turbine planetary gearbox. For experimental evaluation, the gear characteristic instantaneous frequency curves on time-frequency plane are clearly pinpointed in both local and distributed sun gear fault cases. For in situ evaluation, the periodical impulses due to planet gear fault are also clearly identified. The results verify the feasibility and effectiveness of reassigned wavelet scalogram in planetary gearbox fault diagnosis under nonstationary conditions.

High Resolution Time-Frequency Analysis Base on Ricker Atom Matching Pursuit Decomposition

2013 ◽  
Vol 284-287 ◽  
pp. 3115-3119
Author(s):  
Wei Song ◽  
Jia Hui Zuo ◽  
Peng Cheng Hu
Keyword(s):  
Frequency Analysis ◽  
Matching Pursuit ◽  
Low Frequency ◽  
Seismic Signal ◽  
Dominant Frequency ◽  
Frequency Resolution ◽  
Frequency Variation ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
The Impact

The high accuracy time-frequency representation of non-stationary signals is one of the key researches in seismic signal analysis. Low-frequency part of the seismic data often has a higher frequency resolution, on the contrary it tends to have lower frequency resolution in the high frequency part. It’s difficult to fine characterize the time-frequency variation of non-stationary seismic signals by conventional time-frequency analysis methods due to the limitation of the window function. Therefore based on the Ricker wavelet, we put forward the matching pursuit seismic trace decomposition method. It decomposes the seismic records into a series of single component atoms with different centre time, dominant frequency and energy, by making use of the Wigner-Ville distribution, has the time-frequency resolution of seismic signal reach the limiting resolution of the uncertainty principle and skillfully avoid the impact of interference terms in conventional Wigner-Ville distribution.

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