Analysis on Signal Extraction under Sudden Changing Noise Interference Based on Time Frequency Analysis

2014 ◽  
Vol 989-994 ◽  
pp. 3810-3813
Author(s):  
Shao Hua Nie
Keyword(s):  
Frequency Analysis ◽  
Signal Extraction ◽  
Transformation Theory ◽  
Time Frequency Analysis ◽  
Noise Interference ◽  
Time Frequency ◽  
Network Intrusion ◽  
Simulation And Experiment ◽  
Non Stationary Signal ◽  
Changing Noise

The illegal signal such as the intrusion signal which wanted to be embedded in the normal signal was analyzed for the extraction, and made the network environment be safe and pure, so the signal detection problem was shown as the key and important problem in the assurance of the safety of the network. The signal processing principle for the non stationary signal was analyzed firstly, and the new signal extraction method based on the Wigner Ville distribution and Hough transformation theory of the time frequency analysis. The original signal was filtered and detected, the spectrum analysis was got as the same time for the testing the detection property in the simulation. Simulation and experiment was implemented on the objects of the network intrusion signal with the real collection in the database. Simulation result shows that the performance of decreasing the noise and filtering is very good. The intrusion signal can be detected and extracted perfectly, and also it shows good value in the analysis of the network safety in practice.

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.

scholarly journals An Improved Time-Frequency Analysis Method for Instantaneous Frequency Estimation of Rolling Bearing

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Zengqiang Ma ◽  
Wanying Ruan ◽  
Mingyi Chen ◽  
Xiang Li
Keyword(s):  
Frequency Analysis ◽  
Instantaneous Frequency ◽  
Frequency Estimation ◽  
Rolling Bearing ◽  
Analysis Method ◽  
Instantaneous Frequency Estimation ◽  
Time Frequency Analysis ◽  
Noise Interference ◽  
Time Frequency ◽  
Frequency Representation

Instantaneous frequency estimation of rolling bearing is a key step in order tracking without tachometers, and time-frequency analysis method is an effective solution. In this paper, a new method applying the variational mode decomposition (VMD) in association with the synchroextracting transform (SET), named VMD-SET, is proposed as an improved time-frequency analysis method for instantaneous frequency estimation of rolling bearing. The SET is a new time-frequency analysis method which belongs to a postprocessing procedure of the short-time Fourier transform (STFT) and has excellent performance in energy concentration. Considering nonstationary broadband fault vibration signals of rolling bearing under variable speed conditions, the time-frequency characteristics cannot be obtained accurately by SET alone. Thus, VMD-SET method is proposed. Firstly, the signal is decomposed into several intrinsic mode functions (IMFs) with different center frequency by VMD. Then, effective IMFs are selected by mutual information and kurtosis criteria and are reconstructed. Next, the SET method is applied to the reconstructed signal to generate the time-frequency representation with high resolution. Finally, instantaneous frequency trajectory can be accurately extracted by peak search from the time-frequency representation. The proposed method is free from time-varying sidebands and is robust to noise interference. It is proved by numerical simulated signal analysis and is further validated by lab experimental rolling bearing vibration signal analysis. The results show this method can estimate the instantaneous frequency with high precision without noise interference.

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.

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