Nonlinear relation mining for maintenance prediction
2011 (English)Conference paper, Published paper (Refereed)
Abstract [en]
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.
Place, publisher, year, edition, pages
New York: IEEE Press, 2011. 1-9 p.
Keyword [en]
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
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hh:diva-14596DOI: 10.1109/AERO.2011.5747581Scopus ID: 2-s2.0-79955787404ISBN: 978-1-4244-7350-2 OAI: oai:DiVA.org:hh-14596DiVA: diva2:404650
Conference
IEEE Aerospace conference 2011, 5-12 march
Note
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2011-03-172011-03-172013-10-15Bibliographically approved