Comparison of Methods for Time-Frequency Analysis of Oil Whip Vibration Signal

2011 ◽  
Vol 211-212 ◽  
pp. 983-987
Author(s):  
Ling Xiang ◽  
Hao Sun
Keyword(s):  
Frequency Distribution ◽  
Frequency Analysis ◽  
Vibration Signal ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Oil Whip ◽  
Redundant Component ◽  
Non Stationary Signal ◽  
Time Frequency Distribution

The signal analysis is important in extracting fault characteristics in fault diagnosis of machinery. To deal with non-stationary signal, time-frequency analysis techniques are widely used. The experiment data of oil whip vibration fault signal were analyzed by different methods, such as short time Fourier transform (STFT), Wigner-Ville distribution (WVD), Wavelet transform (WT) and Hilbert-Huang Transform (HHT). Compared with these methods, it is demonstrated that the time-frequency resolutions of STFT and WVD were inconsistent, which were easy to cross or make signal lower. WT had distinct time-frequency distribution, but it brought redundant component. HHT time-frequency analysis can detect components of low energy, and displayed true and distinct time-frequency distribution. Therefore, it is a very effective tool to diagnose the faults of rotating machinery.

Extension of Traditional Frequency and Research on Time-Frequency Distribution

2010 ◽  
Vol 44-47 ◽  
pp. 2089-2093
Author(s):  
Shu Lin Liu ◽  
Xian Ming Wang ◽  
Hui Wang ◽  
Hai Feng Zhao
Keyword(s):  
Wavelet Transform ◽  
Frequency Distribution ◽  
Frequency Analysis ◽  
Physical Meaning ◽  
The Novel ◽  
Analysis Method ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Novel Approach ◽  
Time Frequency Distribution

The concept of traditional frequency is extended and the concept of local frequency is proposed, which makes the physical meaning of frequency clearer. The wide adaptability of local frequency is also discussed. Moreover, a novel time-frequency analysis method is presented based on local frequency. The time-frequency distribution of continuous triangular wave signal is analyzed by the novel approach. Compared with wavelet transform and Hilbert-Huang transform (HHT), the results show that the concept of local frequency is correct and the novel time-frequency approach is effective.

scholarly journals EMD based Hilbert energy spectrum analysis of adventitious lung wave signals

2017 ◽  
Vol 7 (1.5) ◽  
pp. 122
Author(s):  
Shankar B. B. ◽  
D. Jayadevappa
Keyword(s):  
Energy Spectrum ◽  
Empirical Mode Decomposition ◽  
Frequency Distribution ◽  
Frequency Analysis ◽  
Spectrum Analysis ◽  
Sound Waves ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Time Frequency Distribution

The respiratory adventitious waves are analyzed effectively by time frequency analysis. In this paper, we present a new approach for rectifying the abnormality in adventitious wave. Basically, there are    two types of respiratory sound waves and these are classified as wheezes and crackles. The proposed method utilizes the time frequency analysis using spectrum analysis method. The modified Empirical Mode Decomposition (EMD) called Ensemble Empirical Mode Decomposition (EEMD) to plot energy spectrum of adventitious wave is used in this work. The proposed method decomposes the respiratory adventitious wave into a different Intrinsic Mode Function (IMF). The long and short duration adventitious waves are present in a wheezing subject and this leads to production of non stationary and nonlinear sound waves. The empirical mode decomposition (EMD) decomposes such characteristic waves. The available spectrogram analyzes techniques related to additive expansions and explore amplitude wise time-frequency distribution. The methodology discussed in this context responding greatly even for correlative noise and explores energy spectra in addition to amplitude spectra.  The various IMFs such produced are exhibits the fine details of adventitious wave and thus pattern can be predicted for final residual. The energy spectrum can be viewed as a diagnostic tool for accurate analysis of wheezing pattern. The decomposed frequency patterns indicate the physiological aspects. The instantaneous frequency and Hilbert energy spectrum based on above mentioned a method are employed by IMF to analyze and present the result in time-frequency distribution to explore the characteristics of inherent properties adventitious signals. The Hilbert marginal spectrum has been used to indicate overall energy distribution from each frequency component. Finally, the resultant EMD analysis along with EEMD energy spectrum is better for asthmatic subject and solves mode mixing problems.

Current-Aided Time-Frequency Analysis of Vibration Signals for Gearbox Fault Diagnosis

2018 ◽  
Author(s):  
Xiaotong Tu ◽  
Yue Hu ◽  
Fucai Li
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Current Signal ◽  
Reliable Technique ◽  
Time Frequency Analysis ◽  
Rotating Speed ◽  
Planetary Gearbox ◽  
Time Frequency

Vibration monitoring is an effective method for mechanical fault diagnosis. Wind turbines usually operated under varying-speed condition. Time-frequency analysis (TFA) is a reliable technique to handle such kind of nonstationary signal. In this paper, a new scheme, called current-aided TFA, is proposed to diagnose the planetary gearbox. This new technique acquires necessary information required by TFA from a current signal. The current signal is firstly used to estimate the rotating speed of the shaft. These parameters are applied to the demodulation transform to obtain a rough time-frequency distribution (TFD). Finally, the synchrosqueezing method further enhances the concentration of the obtained TFD. The validation and application of the proposed method are presented by a simulated signal and a vibration signal captured from a test rig.

The Applicated Comparation of Three Signal Analytical Methods for Ocean CSEM Signature

2013 ◽  
Vol 798-799 ◽  
pp. 561-564
Author(s):  
Ji Yu Zhou ◽  
Feng Dao Zhou
Keyword(s):  
Frequency Analysis ◽  
Oil And Gas ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Analysis Technique ◽  
Oil And Gas Resources ◽  
Concentration Degree ◽  
Short Time ◽  
Better Than

Sea is rich in oil and gas resources, the marine controlled source electromagnetic method (CSEM) is a kind of method seabed oil gas geophysical technology rising in recent years. Because of the problem of CSEM about the air wave in the shallow water, the research of time-frequnecy analysis technique is used to suppress the air wave in this paper. The basic idea is: because of the CSEM signals speed are different in the air and submarine, so the time which received by the receiving points are also different through these two kinds of ways. Using the time-frequency analysis technique and theoretical calculation, we can determine which part of the signal is spread over the ocean, so as to suppress the air wave effectively. This paper lists several methods of time-frequency analysis, such as Short-time Fourier transform, W-V distribution, Wavelet transform, Hilbert Huang transform. Through the time-frequency graph,we get the conclusion that HHT is better than others in concentration degree,and W-V distribution is better than STFT.Compared with the original signal, the time-frequency graph is the best in using Smooth Puseudo W-V Distribution.I have a detailed analysis about real case in using SPWVD at last.

Time-frequency analysis of spontaneous pupillary oscillation signals using the Hilbert-Huang transform

2016 ◽  
Vol 30 ◽  
pp. 106-116 ◽  
Author(s):  
Fabiola M. Villalobos-Castaldi ◽  
José Ruiz-Pinales ◽  
Nicolás C. Kemper Valverde ◽  
Mercedes Flores
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency
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