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Proceedings Paper

Fusing diverse monitoring algorithms for robust change detection
Author(s): Kai F. Goebel; Xiao Hu; Neil H. W. Eklund; Weizhong Yan
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Paper Abstract

Change detection is an important task in remotely monitoring and diagnosing equipment and other processes. Specifically, early detection of differences that indicate abnormal conditions has the promise to provide considerable savings in averting secondary damage and preventing system outage. Of course, accurate early detection has to be balanced against the successful rejection of false positive alarms. In noisy environments, such as aircraft engine monitoring, this proves to be a difficult undertaking for any one algorithm. In this paper, we investigate the performance improvement that can be gained by aggregating the information from a set of diverse change detection algorithms. Specifically, we examine a set of change detectors that utilize a variety of different techniques such as neural nets, random forests, and support vector machines. The different techniques have different detection sensitivities and different regression technique that operates well for time series as well as averaging schemes, and a meta-classifiers. We provide results using illustrative examples from aircraft engine monitoring.

Paper Details

Date Published: 18 April 2006
PDF: 11 pages
Proc. SPIE 6242, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006, 62420M (18 April 2006); doi: 10.1117/12.665922
Show Author Affiliations
Kai F. Goebel, GE Global Research (United States)
Xiao Hu, GE Global Research (United States)
Neil H. W. Eklund, GE Global Research (United States)
Weizhong Yan, GE Global Research (United States)

Published in SPIE Proceedings Vol. 6242:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006
Belur V. Dasarathy, Editor(s)

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