ASQ - Electronics and Communications Division

Condition Recognition of Complex Systems Based on Multi-fractal

Abstract: 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must first be obtained from the IEEE.

Multifractal analysis is applied to extract nonlinear features from complex systems for condition recognition. Abnormal condition is hazardous for process industry complex system which may lead to accidents. Comparing with traditional techniques of condition recognition without concerning nonlinearity of complex system, multifractal spectrum elaborately reveals scale-invariance or self-similarity properties of observed data, which is one of the intrinsic characteristics of complex system. By using Multifractal Detrended Fluctuation Analysis (MF-DFA) algorithm, multifractal spectrum is calculated directly from monitoring time series data. The shape of multifractal spectrum is used to distinguish abnormal conditions from normal ones of complex system. After multi-source information fusion based on Dempster-Shafer evidence theory, the proposed approach can be used for abnormal condition recognition in process industry complex system where continuous multi-channel data are monitored. The effectiveness of the approach is illustrated using data from a simulated dataset and a chemical plant model where potential abnormal conditions are detected effectively, thus avoid severe system safety problems.

Keywords: RAMS 2011 Proceedings - Statistics - Systems Engineering

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