View source for Publications:Nonlinear relation mining for maintenance prediction

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{{PublicationSetupTemplate|Author=Ahmed Mosallam, Stefan Byttner, Magnus Svensson, Thorsteinn Rögnvaldsson
|PID=404650
|Name=Mosallam, Ahmed (Örebro University);Byttner, Stefan (stefan) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), Laboratoriet för intelligenta system (6703));Svensson, Magnus (magsveARC13) (Volvo Technology);Rögnvaldsson, Thorsteinn (denni) (0000-0001-5163-2997) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), Laboratoriet för intelligenta system (6703))
|Title=Nonlinear relation mining for maintenance prediction
|PublicationType=Conference Paper
|ContentType=Refereegranskat
|Language=eng
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|Conference=IEEE Aerospace conference 2011, 5-12 march
|StartPage=1
|EndPage=9
|Year=2011
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|City=New York
|Publisher=IEEE Press
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|ISBN=978-1-4244-7350-2
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|DOI=http://dx.doi.org/10.1109/AERO.2011.5747581
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|ScopusId=2-s2.0-79955787404
|NBN=urn:nbn:se:hh:diva-14596
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|Keywords=fault detection;fault isolation;hierarchical clustering;information theory;machine data mining;maintenance prediction;mechatronic system;nonlinear relation mining;self organizing autonomic computing system;self organizing map;symmetrical uncertainty measurement;wear prediction
|Categories=Datorsystem (20206)
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|Notes=<p>©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.</p>
|Abstract=<p>This paper presents a method for mining nonlinear relationships in machine data with the purpose of using such relationships to detect faults, isolate faults and predict wear and maintenance needs. The method is based on the symmetrical uncertainty measure from information theory, hierarchical clustering and self-organizing maps. It is demonstrated on synthetic data sets where it is shown to be able to detect interesting signal relations and outperform linear methods. It is also demonstrated on real data sets where it is considerably harder to select small feature sets. It is also demonstrated on the real data sets that there is information about system wear and system faults in the detected relationships. The work is part of a long-term research project with the aim to construct a self-organizing autonomic computing system for self-monitoring of mechatronic systems.</p>
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|CreatedDate=2011-03-17
|PublicationDate=2011-03-17
|LastUpdated=2013-10-15
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:404650}}
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