Time–frequency analysis

About: Time–frequency analysis is a research topic. Over the lifetime, 5407 publications have been published within this topic receiving 104346 citations.

Nonlinear and chaotic behavior of structural system investigated by wavelet transform techniques

Abstract: Wavelet transform (WT) is a method that converts a time response presentation in 1D-space into a time-frequency response in 2D-space The main advantages of this transformation are its amplified functional and its 2D analysis presentation The present study is focused on investigating the nonlinear and chaotic behaviors in the time–frequency domain presentation by using wavelet transform The dynamic information from the time–frequency domain is very useful and important for an understanding of the dynamic behavior and control of nonlinear and chaotic systems The present study results show that: (1) WT can well isolate weak sub-harmonics or higher-harmonics from the fundamental harmonic response; (2) the random property of chaotic response in the time–frequency domain can easily be observed by the wavelet transform even for a set of short recorded response data; (3) dynamic behavior of the response phase in the time and frequency domain can be observed by its WT The proposed method is illustrated by investigating the dynamic behavior of a five-degree-of-freedom Duffing's structural system, and the chaotic response is simulated by a single-degree-of-freedom Duffing's system

High-Resolution Bistatic ISAR Image Formation for High-Speed and Complex-Motion Targets

Abstract: The existing bistatic inverse synthetic aperture radar (Bi-ISAR) imaging methods usually uses a “stop-and-go” assumption where the target can be considered not in motion (stop condition) during the fast-time and in motion (go condition) during the slow time. However, for the high-speed target, this assumption is violated; furthermore, the conventional compression via Fourier transform is also invalid due to the quadratic phase term induced by the high-speed motion. In this case, a range compression method using the fractional Fourier transform (FrFT) based on minimum entropy criterion is presented to obtain high-resolution one-dimensional (1-D) range profile. Moreover, to achieve azimuth focusing for the complex-motion target, a new method of imaging time selection based on frequency smooth degree (FSD) is proposed. Simulated and real data are provided to verify the effectiveness of the proposed method.

Fault detection of gearbox from vibration signals using time-frequency domain averaging

Abstract: The vibration signal of a gearbox carries the signature of the fault in the gears, and early fault detection of the gearbox is possible by analyzing the vibration signal using different signal processing techniques. Time domain average can extract the periodic waveforms of a noisy vibration signal, whereas wavelet transformation is able to characterize the local features of the signal in different scales. This paper proposes a new technique, time-frequency domain average, which combines the time domain average and wavelet transformation together to extract the periodic waveforms at different scales from noisy vibration signals. The technique efficiently cleans up noise and detects both local and distributed faults simultaneously. A pilot plant case study has been presented to demonstrate the efficacy of the proposed technique.

Wavelet packet based transient signal classification

Abstract: Nonstationary signals are not well suited for detection and classification by traditional Fourier methods. An alternate means of analysis needs to be used so that valuable time-frequency information is not lost. The wavelet packet transform is one such time-frequency analysis tool. The authors summarize efforts which examine the feasibility of applying the wavelet packet transform to automatic transient signal classification through the development of a classification algorithm for biologically generated underwater acoustic signals in ocean noise. The formulation of a wavelet-packet based feature set specific to the classification of snapping shrimp and whale clicks is given. >

Low frequency shadow detection of gas reservoirs in time-frequency domain

Abstract: In order to depict the local fine structure of seismic signal,implement instantaneous spectral decomposition of 3D seismic data with high computational efficiency and detect low frequency shadow related to gas reservoirs,a generalized S transform(GST)was presented in the paper.GST alters the fixed wavelet basis of existing S transform by introducing two parameters.The wavelet basis of GST is adjustable in accordance with the real application.The results in the simulation of signal model demonstrate that GST is more adaptive with more excellent time-frequency localization.The mechanism of low frequency shadow is introduced here.GST is applied in instantaneous spectral decomposition of real 3D seismic data.The method can not only detect low frequency shadow but also indicate the lithologic edge and spatial distribution of gas reservoirs,which decreases the uncertainty in gas reservoirs detection.