Affiliation:
1. Turbomachinery Group, University of Liège, Chemin des Chevreuils 1, 4000 Liège, Belgium
Abstract
Engine health monitoring has been an area of intensive research for many years. Numerous methods have been developed with the goal of determining a faithful picture of the engine condition. On the other hand, the issue of sensor selection allowing an efficient diagnosis has received less attention from the community. The present contribution revisits the problem of sensor selection for engine performance monitoring within the scope of information theory. To this end, a metric that integrates the essential elements of the sensor selection problem is defined from the Fisher information matrix. An example application consisting in a commercial turbofan engine illustrates the enhancement that can be expected from a wise selection of the sensor set.
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering
Cited by
16 articles.
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