Affiliation:
1. University of Lie`ge, Lie`ge, Wallonia, Belgium
Abstract
Kalman filters are widely used in the turbine engine community for health monitoring purpose. This algorithm gives a good estimate of the engine condition provided that the discrepancies between the model prediction and the measurements are zero-mean, white random variables. However, this assumption is not verified when instrumentation (sensor) faults occur. As a result, the identified health parameters tend to diverge from their actual values which strongly deteriorates the diagnosis. The purpose of this contribution is to blend robustness against sensor faults into a tool for performance monitoring of jet engines. To this end, a robust estimation approach is considered and a Sensor Fault Detection and Isolation module is derived. It relies on a quadratic program to estimate the sensor faults and is integrated easily with the original diagnosis tool. The improvements brought by this robust estimation approach are highlighted through a series of typical test-cases that may be encountered on current turbine engines.
Cited by
1 articles.
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