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Nonlinear relation mining for maintenance prediction
Mosallam, Ahmed, Byttner, Stefan, Svensson, Magnus, Rögnvaldsson, Thorsteinn
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.
Nyckelord: 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