Diagnostic of machine using the timefrequency representation of vibration is becoming widely used by many companies and experts. In recent years, the preventive maintenance is no more desirable and in fact it has evolved into the prediction maintenance. The current methods of machine diagnosis can not provide detailed diagnostics and condition prediction. This paper proposes the application of ...

One basic problem in business-cycle studies is how to deal with nonstationary time series. The market economy is an evolutionary system. Economic time series therefore contain stochastic components that are necessarily time dependent. Traditional methods of business cycle analysis, such as the correlation analysis and the spectral analysis, cannot capture such historical information because the...

where w(τ) is a real-valued even lag window. Implementations (hardware and software) of this transform are already widely present in practice. The STFT is linear and very simple for realization. However, it has some serious drawbacks. The most important one lies in its low concentration in the TF plane, when highly nonstationary signals are analyzed. In order to improve TF representation, vario...

This paper presents the essential elements for developing objective methods of assessment of the performance of time-frequency signal analysis techniques. We define a measure for assessing the resolution performance of time-frequency distributions (TFDs) in separating closely spaced components in the time-frequency domain. The measure takes into account key attributes of TFDs, such as component...

A method is presented for extrapolation of time-series which contain time-varying frequency components. The time-series is complex-demodulated at a set of frequencies. The resulting time-frequency time-series are assumed to be time-dependent such that the amplitude and phase change relatively slowly with time. This change is taken into account in the extrapolation. This model of a non-stationar...

We describe new techniques to detect and analyze periodic motion as seen from both a static and moving camera. By tracking objects of interest, we compute an object’s self-similarity as it evolves in time. For periodic motion, the self-similarity measure is also periodic, and we apply Time-Frequency analysis to detect and characterize the periodic motion. The periodicity is also analyzed robust...