Aircraft engine sensor fault diagnostics using an on-line OBEM update method.

Area of Science:

• Aerospace Engineering
• Control Systems
• Signal Processing

Background:

• Aircraft sensor Fault Detection and Isolation (FDI) systems rely on accurate On-Board Engine Models (OBEMs).
• Rapid engine degradation can cause health condition mismatches, degrading OBEM and FDI performance.
• Simultaneous online estimation and OBEM updating can reduce FDI accuracy.

Purpose of the Study:

• To propose a novel method for updating the online health reference baseline of an OBEM.
• To maintain the effectiveness of an in-flight aircraft sensor FDI system during rapid engine degradation.
• To ensure FDI estimation accuracy is not compromised during OBEM updates.

Main Methods:

• Incorporation of a Hybrid Kalman Filter (HKF) within the FDI system.
• Development of a channel controller method for updating the nonlinear OBEM's health reference baseline.
• Simulations utilizing a turbojet engine Linear-Parameter Varying (LPV) model.

Main Results:

• The proposed channel controller method effectively updates the OBEM's health reference baseline.
• The updated OBEM maintains the performance of the FDI system despite substantial engine degradation.
• The channel controller ensures the update process is completed without interference from single sensor faults.

Conclusions:

• The developed method successfully addresses the challenge of OBEM updating in dynamic aircraft engine conditions.
• The channel controller provides a robust solution for maintaining FDI system accuracy and reliability.
• This approach enhances the overall safety and operational efficiency of aircraft.