A power spectrum characterizes frequency content and resonances within a system. Because degradation usually causes changes in the spectral signature, spectral behavior provides a rich source of information for feature generation.
Select from parametric and nonparametric algorithms. For the parametric methods, Diagnostic Feature Designer fits a parametric model to the signal. The software then uses this model to compute the spectral density. You can choose an auto-regressive (AR) model or a state-space model.
Signal — Source signal for the power spectrum.
Frequency — Frequency settings for the frequency axis. To set these values manually, clear Select automatically and update the parameters for the frequency vector generation.
Auto-regressive model — The app fits an AR model to the
signal and uses this model to compute the spectral density. For information on setting
model order, approach, and windowing method, see
State-space model — The app fits a state-space model to the signal and uses this model to compute the spectral density.
Model Order — Specify the model order directly, or specify a range of orders for automatic order selection. With automatic order selection, the software automatically selects the smallest order that leads to a good fit to the data.
Improve results using nonlinear least squares
search — Selecting this option improves estimation results for
specific scenarios, at the cost of additional computational time. For more
information, see the
'SearchMethod' option in
Maximum number of iterations — Increase the number of iterations to improve result accuracy. Decrease the number to improve computational speed.
For more information on state-space modeling, see
Welch's method — The app calculates the power spectrum
from the source signal using Welch's method. For information on setting window
The software stores the results of the computation in a new variable. The new variable
name includes the source signal name with the suffix