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Proceedings Paper

Automated approach to trend monitoring based on fractal analysis
Author(s): Scott A. Starks; James Hamilton; Nitin Okhade
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Paper Abstract

As systems become more complex, the monitoring and interpretation of measurement data related to the health of the system becomes increasingly more difficult. Trend monitoring is an important task that involves a prediction of the future state of system health based upon past observations. In many systems, sensors or suites of sensors gather data about the state of health of the system and its processes. Analysis of the power spectrum of the time series resulting from this sort of data collection provides insight into the trends inherent. In this paper, we present a fractal-based approach to the interpretation of the power spectrum of the time series. Using fractal analysis enables the characterization of the power spectrum using a minimal set of parameters. A computational algorithm for the calculation of these parameters is presented and shows promise as a basis for trend monitoring.

Paper Details

Date Published: 20 August 1993
PDF: 6 pages
Proc. SPIE 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques, (20 August 1993); doi: 10.1117/12.150160
Show Author Affiliations
Scott A. Starks, Univ. of Texas/El Paso (United States)
James Hamilton, Univ. of Texas/El Paso (United States)
Nitin Okhade, Univ. of Texas/El Paso (United States)


Published in SPIE Proceedings Vol. 2055:
Intelligent Robots and Computer Vision XII: Algorithms and Techniques
David P. Casasent, Editor(s)

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