Which analysis technique utilizes the Fast Fourier Transform (FFT)?

The utilization of the Fast Fourier Transform (FFT) is fundamentally associated with frequency domain analysis. This technique processes a time-domain signal and transforms it into its constituent frequencies, effectively representing how much of each frequency is present in the original signal.

In frequency domain analysis, the primary objective is to examine the frequency content of a signal, which is where the FFT comes into play. By applying FFT, analysts can detect and quantify periodicities and other frequency-based characteristics within the data. This capability is crucial in vibration analysis, as it allows for the identification of resonances, harmonics, and potential faults in machinery, which may not be evident in the time domain.

The other options revolve around different methods of signal examination. Time domain analysis focuses on the signal as it varies over time, without the advantage of frequency decomposition that the FFT provides. Wavelet analysis, while also acknowledging frequency, deals with time-localization, allowing the analysis of non-stationary signals in more detail. Statistical analysis pertains to data interpretation using statistical methods rather than frequency-related aspects. Therefore, frequency domain analysis is clearly the appropriate technique linked to the Fast Fourier Transform in this context.