Papers by Keyword: Time-Frequency Analysis (TFA)

Abstract: he purpose of this paper was to perform optimize the best wavelet basis function and TFA (Time frequency analysis) techniques on a target, in order to provide high-resolution instant spectrum data to help in the fluid detection. Wavelet transform is an effective tool to calculate the frequency. And spectral decomposition technique can depict the frequency characters of seismic reflection that are caused by fluid. In order to optimize the best wavelet basis function, different wavelet basis was tested on a sin model to determine the optimum parameters on the noised-sinusoidal model. Several wavelet bases were tested for the frequency recognition capability on the model, and then the optimum wavelet base function was used in the subset of the seismic data. The optimal wavelet basis was selected to test in the subset of the seismic data, strong amplitude anomaly showed. And so may be use the well-log interpretation result to guarantee that the strong amplitude anomaly have effects at the target.

Abstract: The propagation of scattering Lamb wave in plate was simulated using transient dynamic analysis in ANSYS. In order to extract the characteristic information of received signal for damage identification, the short time Fourier transform based on time-frequency analysis was utilized, and then the energy distribution and envelop of received signal were obtained. Based on the displacement contour of simulation and energy distribution, the propagation of scattering wave in plate with a through hole was examined. Also, a mathematic relationship between damage location and scattering signal was developed, with the help of wave propagation path through actuator, damage and sensor. A nonlinear optimization method was applied on the mathematic relationship to obtain the damage location. The damage identification method using scattering Lamb wave was therefore established.

Abstract: Tool wear monitoring plays an important role in the automatic machining processes. Therefore, it is necessary to establish a reliable method to predict tool wear status. In this paper, features of acoustic emission (AE) extracted from time-frequency domain are integrated with force features to indicate the status of tool wear. Meanwhile, a support vector machine (SVM) model is employed to distinguish the tool wear status. The result of the classification of different tool wear status proved that features extracted from time-frequency domain can be the recognize-features of high recognition precision.

Abstract: The micro Doppler effect of the radar echo signal of helicopter rotor is studied, and the formula of helicopter rotor echo is obtained. Then the received echo signal of helicopter rotor simulated is analyzed in time domain, frequency domain and time-frequency domain respectively, the analysis results show that it is a good method to extract micro Doppler of helicopter rotor echo by time-frequency analysis. According to analysis results, obtained a method to determine parity of blades and velocity of helicopter rotor, these methods can be used to identify helicopter.

Abstract: Shed-tunnel is one of common prevention measures along the highway. Through the wavelet theory we denoised the rockfall impact signal when the rock impact the ordinary shed-tunnel and the energy dissipation shed-tunnel. And then we evaluated the wavelet theory’s denoise effect by the signal-to-noise ratio. The calculation result indicated that the denoise effect is very good. At last, through the autocorrelation analysis and time-frequency analysis for the rockfall impact signal, it was found that the ordinary shed-tunnel’s impact signals didn’t have obvious frequency and the frequency contained many component,but the energy dissipation shed-tunnel’s impact signals had obvious frequency. So the energy dissipation shed-tunnel’s impact signals had a relatively fixed cycle and frequency. The received frequency of rockfall impact by the time-frequency analysis could provide the basis for the design of energy dissipation shed-tunnel’s natural frequency.

Abstract: Gearbox vibrations are random cyclostationary signals which are a combination of periodic and random processes due to the machine’s rotation cycle and interaction with the real world. The combinations of such components are best considered as cyclostationary. This paper discusses which second order cyclostationary statistics should be used for fault diagnosis of gear crack. The second order cyclostationary statistical methods are firstly introduced and then applied to fault diagnosis of gear crack. This approach is capable of completely extracting the characteristic fault frequencies related to the defect. Experiment results show that the second order cyclostationary statistics is powerful and effective in feature extracting and fault detecting for gearbox. The experimental result shows that the second order cyclostationary statistics can effectively diagnosis gear localized crack fault.

Abstract: Empirical mode decomposition (EMD), a new self-adaptive signal processing method, has been recently developed for nonlinear and non-stationary time series analysis. In this paper, EMD method is described and applied in time-frequency analysis. Aiming at the problems of intrinsic mode function (IMF) criterion in the EMD method, neural network (NN) prediction model and wavelet packet transform (WPT) technology are simultaneously introduced into the EMD method to improve the border effect and to enhance the ability of signal analysis, and thus a hybrid EMD-based time-frequency analysis strategy is proposed. The simulated time series are exploited to verify the effectiveness of the proposed hybrid model. Experimental results indicate that the hybrid strategy gives a quite satisfactory performance when both NN prediction model and WPT method are employed.

Abstract: Because the faults happening in the motor (such as the stator and the rotor faults) can distort the sinusoidal response of the motor RPM and the main frequency, hence the spectrum method has previously been introduced which it relates to both amplitudes and phases among harmonics in a signal. The method popularly applied for fault detection is based on frequency analysis by observing the side band, its harmonics around main frequencies or its other harmonics. Based on the present experiments, the spectrum method by FFT function has ability to distinguish the motor condition. But, the fault severity levels seem to not able to analyze. Hence the time-frequency Analysis (or spectrogram) of the stator phase currents is proposed here. The method is expected to show relation between the phase current signals and the fault levels which make it can detect the faults and also indicate the fault levels. The experiments show that the proposed method can provide good accuracy for fault prediction and fault level quantification. Hence it can conclude that the propose method can be an effective tool for motor fault prediction.

Abstract: 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.

Abstract: The notion of orthogonal vector-valued binary wavelet packs is introduced. Their traits is investigated by virtue of time-frequency analysis method and finite group theory. Orthogonality formulas are established. Orthonormal wavelet pack bases are obtained. A novel method for constructing a kind of orthogonal shortly supported vector-valued wavelets is presented.