Novel approaches for the estimation of the spectrum background for stationary and quasi-stationary signals

Omri Matania, Renata Klein, Jacob Bortman

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The vibration signals of rotating machinery contain information about the rotating components and the machine's structure. The peaks of a vibration spectrum are related to the vibration signals of the rotating components, and the background may be regarded as the spectrum without the “peaks”. In this paper, we extend studies on the estimation of the spectrum background. Two topics are addressed: first, an analysis of known methods and their extension into a new autonomic algorithm (autonomic Ceps-ACS) for background estimation of stationary signals, and second, a new approach for estimation of the background for quasi-stationary signals. The estimation of the background in both cases is based on two current techniques, namely, liftering of low quefrencies in the cepstrum domain (Ceps-Lift) and adaptive clutter separation (ACS). A relationship between the parameters of Ceps-Lift and ACS was formulated, enabling the development of the autonomic Ceps-ACS algorithm. For quasi-stationary signals, both Ceps-Lift and ACS have limited ability to estimate the spectrum background. To address the topic of estimation of the spectrum background of quasi-stationary signals, we proposed a novel approach that uses the background in the order domain to estimate the background in the frequency domain. Experimental measured transfer functions, measured data and simulated vibration signals were used to demonstrate the performance of the algorithms.

Original languageEnglish
Article number108503
JournalMechanical Systems and Signal Processing
Volume167
DOIs
StatePublished - 15 Mar 2022

Keywords

  • Adaptive clutter separation (ACS)
  • Background estimation
  • Cepstrum-liftering
  • Nearest neighbor
  • Quasi-stationary signal

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications

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