Adaptive learning for event modeling and characterization

Document Type

Article

Publication Date

5-1-2007

Abstract

Adaptive learning of specific patterns or events of interest has been an area of significant research for various applications in the last two decades. In developing diagnostic evaluation and safety monitoring applications of a propulsion system, it is critical to detect, characterize and model events of interest. It is a challenging task since the detection system should allow adaptive characterization of potential events of interest and correlate them to learn new models for future detection for online health monitoring and diagnostic evaluation. In this paper, a novel framework is established using a hierarchical adaptive clustering approach with fuzzy membership functions to characterize specific events of interest from the measured and processed features. Raw engine measurement data is first analyzed using the wavelet transform to provide features for localization of frequency information for use in the classification system. A method combining hierarchical and fuzzy k-means clustering is then applied to a set of selected measurements and computed features to determine the events of interest during engine operations. Experimental results have shown that the proposed approach is effective and computationally efficient to detect, characterize and model new events of interest from data collected through continuous operations. © 2006 Pattern Recognition Society.

Identifier

33846267244 (Scopus)

Publication Title

Pattern Recognition

External Full Text Location

https://doi.org/10.1016/j.patcog.2006.09.018

ISSN

00313203

First Page

1544

Last Page

1555

Issue

5

Volume

40

Fund Ref

National Aeronautics and Space Administration

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