Known Unknowns: Novelty Detection in Condition Monitoring

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In time-series analysis it is often assumed that observed data can be modelled as being derived from a number of regimes of dynamics,as e.g. in a Switching Kalman Filter (SKF) [8, 2]. However, it may not be possible to model all of the regimes, and in this case it can be useful to represent explicitly a ‘novel’ regime. We apply this idea to the Factorial Switching Kalman Filter (FSKF) by introducing an extra factor (the ‘Xfactor’) to account for the unmodelled variation.We apply our method to physiological monitoring data from premature infants receiving intensive care, and demonstrate that the model is effective in detecting abnormal sequences of observations that are not modelled by the known regimes.
Original languageEnglish
Title of host publicationPattern Recognition and Image Analysis
Subtitle of host publicationThird Iberian Conference, IbPRIA 2007, Girona, Spain, June 6-8, 2007, Proceedings, Part I
EditorsJoan Martí, José Miguel Benedí, Ana Maria Mendonça, Joan Serrat
PublisherSpringer Berlin Heidelberg
Number of pages6
ISBN (Electronic)978-3-540-72847-4
ISBN (Print)978-3-540-72846-7
Publication statusPublished - 2007

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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