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.
Highlights
IntroductionCondition-based maintenance has been widely promoted in the jet engine community
In the last years, condition-based maintenance has been widely promoted in the jet engine community
The problem of optimal selection of the sensor configuration for diagnostics has been revisited from the viewpoint of information theory
Summary
Condition-based maintenance has been widely promoted in the jet engine community. A maintenance schedule adapted to the level of deterioration of the engine leads to many advantages such as improved operability and safety or reduced life cycle costs. In this framework, generating a reliable information about the health condition of the engine is a requisite. Its purpose is to assess the changes in engine module performance on the basis of measurements collected along the gas path of the engine [1]. This approach allows to track the evolution of a particular engine relative to some baseline performance which may be engine specific or fleet averaged.
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