Spectral Analysis

Spectral Analysis TAB enables you to analyze the spectral components of the dataset by measuring the strength of periodic (sinusoidal) components of each signal in a dataset at different frequencies. Spectral analysis is another way of looking at the same data where instead of observing the dataset in the time domain, you can analyze the dataset in the frequency domain. You can compute Fast Fourier Transform(FFT), Power spectral density(PSD), or complex cepstrum(CCEPS) of a signal and visualize the corresponding results.

Note

This tutorial assumes that you already selected a project and imported data. For more information please visit on Project and Import Data section.

User Interface Structure

This section helps to familiar with the Spectral Analysis TAB interface elements.

1. Signals

Choose input dataset in the signals (multi-select possible). It can only values type signal or value-vector pairs type signal where value-vector pair means Time-series signal. The vector represents the time axis in Time-series data. If the value-vector pair type signal is selected, the system collects sampling frequency (fs) information from the vector information; otherwise, the system will automatically consider the sampling frequency (fs) as 1.

2. Choose method

Select the desired method that you want to use. There are three options available in the Reference section.

3. Preview/Apply

Preview button will do the operation but will not save the data, while Apply will give you the option to save the data in a folder in content. More information is available in here

Basic Usage

Using different Spectral Analysis operations is simple and always follows the same rules:

  1. Choose input datasets in the signals
  2. Choose an analysis method.
  3. Preview/ Apply operation

The following animation shows the way to calculate the PSD of a signal:

Image_Caption

Filter

In all Data Enrichment Tabs, you can select only a part of the data using a Filter. A more detailed description of Filters can is available here

References

  • FFT: The FFT reveals frequency components of a time signal by representing it in frequency space after decomposing the time data in the series of sinus waves. You can read more about FFT here, and the details of the scipy package are here
  • PSD: The PSD of a time series describes the distribution of power into frequency components composing that signal, and it calculates using the welch method. More about scipy implementation is here
  • Complex Cepstrum Cepstrum Analysis is a method for detecting periodicity in a frequency spectrum. You can read more about Complex Cepstrum here