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DOI: http://dx.doi.org/10.7551/978-0-262-32621-6-ch117
Pages 734-735
First published 30 July 2014

Optimizing the crossregulation model for scalable abnormality detection

Danesh Tarapore, Pedro U. Lima, Jorge Carneiro and Anders Lyhne Christensen

Abstract (Excerpt)

The engineering of fault-detection systems for multirobot systems (MRS) is a well-studied problem (e.g, Christensen et al. (2009)). Most fault-detection models are built on the assumption that normal behavior is known, and can be characterized in advance. The models are trained to recognize predefined normal behaviors, and behaviors not recognized are labeled abnormal. While such an approach does provide some interesting results of robust fault detection and fault tolerance, they may not be applicable when normal behavior can change as a result of unforeseen environmental conditions and online learning for instance. Furthermore, prior information required to characterize normal behaviors, may not always be available.