Difference between revisions of "Publications:Nonlinear relation mining for maintenance prediction"
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{{PublicationSetupTemplate|Author=Ahmed Mosallam, Stefan Byttner, Magnus Svensson, Thorsteinn Rögnvaldsson | {{PublicationSetupTemplate|Author=Ahmed Mosallam, Stefan Byttner, Magnus Svensson, Thorsteinn Rögnvaldsson | ||
|PID=404650 | |PID=404650 | ||
| − | |Name=Mosallam, Ahmed (Örebro University);Byttner, Stefan | + | |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 | |Title=Nonlinear relation mining for maintenance prediction | ||
|PublicationType=Conference Paper | |PublicationType=Conference Paper | ||
Latest revision as of 21:40, 30 September 2016
| Title | Nonlinear relation mining for maintenance prediction |
|---|---|
| Author | Ahmed Mosallam and Stefan Byttner and Magnus Svensson and Thorsteinn Rögnvaldsson |
| Year | 2011 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | |
| Conference | IEEE Aerospace conference 2011, 5-12 march |
| DOI | http://dx.doi.org/10.1109/AERO.2011.5747581 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:404650 |
| Abstract | 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. |
