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Fast Averaged Cyclic Periodogram method to compute spectral correlation and coherence.

Jaafar K Alsalaet1

  • 1Department of Mechanical Engineering, College of Engineering, University of Basrah, Basrah, Iraq.

ISA Transactions
|February 11, 2022
PubMed
Summary
This summary is machine-generated.

A new fast method for Averaged Cyclic Periodogram (ACP) calculation enhances cyclostationary signal analysis. This technique improves spectral correlation and coherence estimation, even with noise, and is memory-efficient.

Keywords:
Averaged cyclic periodogramBearing defectsCyclostationary signalsFast ACPSpectral coherenceSpectral correlation

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Area of Science:

  • Signal Processing
  • Data Analysis

Background:

  • Cyclostationary signal analysis is vital in communications, meteorology, and vibration analysis.
  • Spectral correlation and coherence are key for detecting cyclostationarities amidst noise.

Purpose of the Study:

  • To introduce a computationally efficient method for calculating the Averaged Cyclic Periodogram (ACP).
  • To improve the analysis of cyclostationary and cyclo-non-stationary signals.

Main Methods:

  • A novel approach for ACP calculation using shifted Fourier transforms.
  • Efficiently extracts spectral correlation and coherence across the full frequency range.

Main Results:

  • The proposed method significantly reduces computation time compared to existing techniques.
  • Demonstrates accuracy and applicability on simulated and real vibration signals.
  • Requires minimal memory, suitable for resource-constrained platforms.

Conclusions:

  • The developed ACP method offers a faster, memory-efficient solution for cyclostationary signal analysis.
  • Applicable to both stationary and non-stationary signals with constant or variable cyclic intervals.