Memorial University DAI: Time series novelty detection with application to production sensor systems

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Title	Time series novelty detection with application to production sensor systems
Author	Anstey, Jonathan S. (Jonathan Skanes), 1981-
Description	Thesis (M.Eng.)--Memorial University of Newfoundland, 2011. Engineering and Applied Science
Date	2011
Pagination	xviii, 156, [4] leaves : ill., +1 CD-ROM (4 3/4 in.)
Subject	Manufacturing processes--Newfoundland and Labrador--Automation; Manufacturing industries--Newfoundland and Labrador--Automation; Rug and carpet industry--Newfoundland and Labrador--Automation; Pattern recognition systems; Novelty fabrics--Newfoundland and Labrador; Algorithms
Degree	M.Eng.
Degree Grantor	Memorial University of Newfoundland. Faculty of Engineering and Applied Science
Discipline	Engineering and Applied Science
Language	Eng
Spatial Coverage	Canada--Newfoundland and Labrador
Notes	Bibliography: leaves 148-156.
Abstract	Modern fiber manufacturing plants rely heavily on the use of automation. Automated facilities use sensors to measure fiber state and react to data patterns, which correspond to physical events. Many patterns can be predefined either by careful analysis or by domain experts. Instances of these patterns can then be discovered through techniques such as pattern recognition. However, pattern recognition will fail to detect events that have not been predefined, potentially causing expensive production errors. A solution to this dilemma, novelty detection, allows for the identification of interesting data patterns embedded in otherwise normal data. In this thesis we investigate some of the aspects of implementing novelty detection in a fiber manufacturing system. Specifically, we empirically evaluate the effectiveness of currently available feature extraction and novelty detection techniques on data from a real fiber manufacturing system. -- Our results show that piecewise linear approximation (PLA) methods produce the highest quality features for fiber property datasets. Motivated by this fact, we introduced a new PLA algorithm called improved bottom up segmentation (IBUS). This new algorithm produced the highest quality features and considerably more data reduction than all currently available feature extraction techniques for our application. -- Further empirical results from several leading time series novelty detection techniques revealed two conclusions. A simple Euclidean distance based technique is the best overall when no feature extraction is used. However, when feature extraction is used the Tarzan technique performs best.
Type	Text
Resource Type	Electronic thesis or dissertation
Format	Image/jpeg; Application/pdf
Source	Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries
Accompanying Files	http://collections.mun.ca/theses_extras/Anstey_JonathanS.zip
Rights	The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.
Collection	Electronic Theses and Dissertations
Scanning Status	Completed
PDF File	(5.91 MB) -- http://collections.mun.ca/PDFs/theses/Anstey_JonathanS.pdf
CONTENTdm file name	39038.cpd