A Sensor-Fault-Tolerant Diagnosis Tool Based on a Quadratic Programming Approach

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 zeromean, 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.