Transient analysis functions

• Gabor analysis
• Short-time Fourier transform
• Wigner-Ville distribution
• Analytic wavelet scalogram
• Time-varying filter
• Wavelet-based noise reduction
• Mean instantaneous frequency

You can seletc different algorithms to perform transient signal analysis.

Clarify pattersn of frequency change

One major benefit of applying the Transient Analysis Module to a signal is to discover the pattern of frequency changes, which often clarifies the nature of the signal. The figure below shows the Gabor spectrogram of a signal obtained from a fluid-film bearing machine during a starting-up and shutting-down test. The frequencies associated with the running speed (1X) as well as the harmonics are clearly seen. When the running speed meets the first resonance frequency of the shaft, the energy reaches a maximum point. The oil whirl and whip are also clearly shown.

Use Gabor spectrpgram to study oil whirl and oil whip.

Time Varying Filter

Different frequency contents of a transient signal may overlap in the frequency domain. You can use time-frequency analysis to distinct time-frequency components, perform time-varying filtering and reconstruct these components for further analysis. The figure below shows the procedure for a machine starting-up signal. On the time-frequency color map, the frequencies associated with the running speed and the harmonics, and the resonance frequency of the bending vibration are clearly distinguishable. Three time-frequency components can be separated using three mask matrices. The random noise is amazingly reduced. Then the three components can be reconstructed to the time domain.

Time-Varying Filter