LPV model-based robust diagnosis of flight actuator faults

• A synthesis methodology of robust gain-scheduling based fault detection and diagnosis (FDD) systems for flight actuators is proposed.
• The proposed generic FDD system architecture allows the simultaneous monitoring of several classes of additive faults of flight actuators.
• Optimal tuning of the parameters of the FDD system (e.g., detection thresholds) guarantees a satisfactory fault monitoring performance.
• Real-time signal processing based fault identification is instrumental for the performance robustness of the overall FDD system.
• The industrial validation on AIRBUS actuator test bench in the case of jamming confirmed the satisfactory performance of the FDD system.

A linear parameter-varying (LPV) model-based synthesis, tuning and assessment methodology is developed and applied for the design of a robust fault detection and diagnosis (FDD) system for several types of flight actuator faults such as jamming, runaway, oscillatory failure, or loss of efficiency. The robust fault detection is achieved by using a synthesis approach based on an accurate approximation of the nonlinear actuator–control surface dynamics via an LPV model and an optimal tuning of the free parameters of the FDD system using multi-objective optimization techniques. Real-time signal processing is employed for identification of different fault types. The assessment of the FDD system robustness has been performed using both standard Monte-Carlo methods as well as advanced worst-case search based optimization-driven robustness analysis. A supplementary industrial validation performed on the AIRBUS actuator test bench for the monitoring of jamming, confirmed the satisfactory performance of the FDD system in a true industrial setting.