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Waterfall plots are often used to show how two-dimensional phenomena change over time. [1] A three-dimensional spectral waterfall plot is a plot in which multiple curves of data, typically spectra, are displayed simultaneously. Typically the curves are staggered both across the screen and vertically, with "nearer" curves masking the ones behind.
Such a chart can be used in turbine design. Experimentally measured vibration response spectrum as a function of the shaft's rotation speed (waterfall plot), the peak locations for each slice usually corresponding to the eigenfrequencies.
Spectrograms of light may be created directly using an optical spectrometer over time.. Spectrograms may be created from a time-domain signal in one of two ways: approximated as a filterbank that results from a series of band-pass filters (this was the only way before the advent of modern digital signal processing), or calculated from the time signal using the Fourier transform.
Waterfall charts can be used for various types of quantitative analysis, ranging from inventory analysis to performance analysis. [4] Waterfall charts are also commonly used in financial analysis to display how a net value is arrived at through gains and losses over time or between actual and budgeted amounts. Changes in cash flows or income ...
However, the spectral density of a small window of a longer signal may be calculated, and plotted versus time associated with the window. Such a graph is called a spectrogram. This is the basis of a number of spectral analysis techniques such as the short-time Fourier transform and wavelets.
The smoothed periodogram is sometimes referred to as a spectral plot. [11] [12] Periodogram-based techniques introduce small biases that are unacceptable in some applications. Other techniques that do not rely on periodograms are presented in the spectral density estimation article.
Spectrum analysis, also referred to as frequency domain analysis or spectral density estimation, is the technical process of decomposing a complex signal into simpler parts. As described above, many physical processes are best described as a sum of many individual frequency components.
The parameter B displayed on each spectral plot is the function's noise equivalent bandwidth metric, in units of DFT bins. [16]: p.56 eq.(16) See spectral leakage §§ Discrete-time signals and Some window metrics and Normalized frequency for understanding the use of "bins" for the x-axis in these plots.