Dynamic Time-Frequency Analysis for Non-Stationary Signal from Mechanical Measurement of Bearing Vibration

2009 ◽  
Author(s):  
Wei Liao ◽  
Pu Han ◽  
Xu Liu
Keyword(s):  
Frequency Analysis ◽  
Mechanical Measurement ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Bearing Vibration ◽  
Dynamic Time ◽  
Non Stationary Signal

Application of the Blind Source Separation Based on Time-Frequency Analysis in Mechanical Fault Diagnosis

2014 ◽  
Vol 945-949 ◽  
pp. 1054-1062 ◽  
Author(s):  
Zhi Nong Li ◽  
Fen Zhang ◽  
Xu Ping He ◽  
Yao Xian Xiao
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Frequency Analysis ◽  
Source Separation ◽  
Spectral Feature ◽  
Frequency Domain Analysis ◽  
Mechanical Equipment ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Non Stationary Signal

Blind source separation provides a new method for the separation of mechanical sources under high level background noise, as well as the diagnosis of the compound fault. At present, the blind source separation has been successfully applied to the mecanical fault diagnosis. But the traditional mechanical source separation methods are restricted to non-gauss, stationary and mutually independent source signals. However, the mechanical fault signals do not suffice to these conditions, and generally exhibit non-stationarity and non-independence. For the non-stationary signal, its spectral feature is time-varying. Thus only the time-domain or frequency-domain analysis is not sufficient to describe the characteristics of non-stationary signal. The time-frequency analysis, which can provide the information about that the spectrum of the signal varies with the time, is a useful tool for non-stationary signal analysis. In this paper, combined time-frequency analysis with blind source separation, a blind source separation method for the non-stationary signal of the mechanical equipment based on time-frequency analysis is proposed and studied. The simulation and experimental results show that the proposed approach is feasible and effective.

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.

Chirp Signal Time-Frequency Analysis Characteristic Comparison

2012 ◽  
Vol 226-228 ◽  
pp. 568-571 ◽  
Author(s):  
Jing Lei Zhou ◽  
Fan Wang
Keyword(s):  
Frequency Analysis ◽  
Chirp Signal ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Signal Noise ◽  
Time Frequency ◽  
Sine Signal ◽  
Non Stationary Signal ◽  
Signal Time ◽  
Selection Of

Chirp signal is a typical non-stationary signal, and have been widely used in communication, sonar, radar and so on. So, this signal is worth to analysis. In order to show the characteristics, this paper first introduces the definition and formula of each algorithm, then with all kinds of time-frequency analysis method to the signals, and the signal to add two sine signal noise are analyzed, the comparison of the characteristics of the method in the paper, and the signal for the analysis, the selection of an appropriate analysis. Through analysis and comparison, when dealing with the signal, Hilbert-Huang transformation not only has a better gathered characteristic, but also has a better resolution to distinguish the sine signal noise. Finally, use the MATLAB software simulation to obtain the result.

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