Fusing diverse monitoring algorithms for robust change detection

Kai F. Goebel; Xiao Hu; Neil H. W. Eklund; Weizhong Yan

doi:10.1117/12.665922

18 April 2006 Fusing diverse monitoring algorithms for robust change detection

Kai F. Goebel, Xiao Hu, Neil H. W. Eklund, Weizhong Yan

Proceedings Volume 6242, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006; 62420M (2006) https://doi.org/10.1117/12.665922
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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.

Citation Download Citation

Kai F. Goebel, Xiao Hu, Neil H. W. Eklund, and Weizhong Yan "Fusing diverse monitoring algorithms for robust change detection", Proc. SPIE 6242, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006, 62420M (18 April 2006); https://doi.org/10.1117/12.665922

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