Novel approaches for the estimation of the spectrum background for stationary and quasi-stationary signals. (15th March 2022)

• Record Type: Journal Article
• Authors: Matania, Omri; Klein, Renata; Bortman, Jacob
• Is Part Of: Mechanical systems and signal processing. Volume 167:Part A(2022)
• Publication Date: 2022-03-15
• Subjects: Background estimation -- Adaptive clutter separation (ACS) -- Cepstrum-liftering -- Quasi-stationary signal -- Nearest neighbor; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108503
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: An algorithm for autonomic estimation of the spectrum background. Theoretical formulation of the relationship of the parameters of ACS and Ceps-Lift. An algorithm for improved estimation of the background for quasi-stationary signals. Mapping of a background from the frequency domain to the order domain. Performance examinations by measured transfer functions and simulated signals. 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.