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Data-driven nonstationary signal decomposition approaches: a comparative analysis.

Thomas Eriksen1, Naveed Ur Rehman2

  • 1Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, 8200, Aarhus, Denmark.

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|January 31, 2023
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Summary
This summary is machine-generated.

Signal decomposition (SD) methods break down complex signals into simpler parts. This study evaluates popular SD algorithms, offering insights into their performance and best practices for parameter selection, aiding in signal processing tasks.

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Area of Science:

  • Signal Processing
  • Data Analysis

Background:

  • Signal decomposition (SD) is crucial for analyzing non-stationary signals.
  • It aids in noise reduction, feature extraction, and understanding underlying systems.
  • Data-driven SD methods like EMD and VMD are widely used without strong prior assumptions.

Purpose of the Study:

  • To comprehensively evaluate popular signal decomposition algorithms.
  • To assess algorithm accuracy with and without noise.
  • To determine sensitivity to parameter changes and identify best practices.

Main Methods:

  • Extensive experiments using synthetic and real-life signals.
  • Evaluation of single- and multi-channel (multivariate) data.
  • Assessment of mode-alignment property in multivariate signals, especially under noisy conditions.

Main Results:

  • Detailed comparison of pros and cons for various SD algorithms.
  • Identification of optimal parameter selection strategies for successful algorithm operation.
  • Performance analysis of SD methods in handling noisy and multivariate data.

Conclusions:

  • Provides a clear understanding of SD algorithm operations and limitations.
  • Offers practical guidance for selecting and applying SD techniques effectively.
  • Highlights best practices for accurate signal decomposition in diverse applications.