]> 2026-04-05T00:57:13+02:00 Publications:Nonlinear relation mining for maintenance prediction 500 en <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> IEEE Aerospace conference 2011, 5-12 march http://dx.doi.org/10.1109/AERO.2011.5747581 http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:404650 9 Conference Paper 1 Nonlinear relation mining for maintenance prediction 2011 2016-09-30T20:40:00Z 2457662.3611111 Nonlinear relation mining for maintenance prediction 500 0 plainlist list 1 [[:Publications:Nonlinear relation mining for maintenance prediction]] Nonlinear relation mining for maintenance prediction# QUERYe08a913af10c902a9acc88c81e0c4021 500 [2,"smw-datavalue-property-restricted-annotation-use","Publisher"] Nonlinear relation mining for maintenance prediction# ERR6c155b5d70b69ddb590b4f2e73389c2a 500 [2,"smw-datavalue-property-restricted-annotation-use","Author"] Nonlinear relation mining for maintenance prediction# ERRe24f17e090c98448f3de99d03182b7b6