Research Article: Aircraft engine sensor fault diagnostics using an on-line OBEM update method

Date Published: February 9, 2017

Publisher: Public Library of Science

Author(s): Xiaofeng Liu, Naiyu Xue, Ye Yuan, Xiaosong Hu.


This paper proposed a method to update the on-line health reference baseline of the On-Board Engine Model (OBEM) to maintain the effectiveness of an in-flight aircraft sensor Fault Detection and Isolation (FDI) system, in which a Hybrid Kalman Filter (HKF) was incorporated. Generated from a rapid in-flight engine degradation, a large health condition mismatch between the engine and the OBEM can corrupt the performance of the FDI. Therefore, it is necessary to update the OBEM online when a rapid degradation occurs, but the FDI system will lose estimation accuracy if the estimation and update are running simultaneously. To solve this problem, the health reference baseline for a nonlinear OBEM was updated using the proposed channel controller method. Simulations based on the turbojet engine Linear-Parameter Varying (LPV) model demonstrated the effectiveness of the proposed FDI system in the presence of substantial degradation, and the channel controller can ensure that the update process finishes without interference from a single sensor fault.

Partial Text

Because fault diagnostics are crucial for flight safety[1], several effective sensor FDI approaches have been developed recently[2, 3], such as fault detection observer in Takagi–Sugeno’s form, a bank of neural networks and so on. However, these approaches did not address how to maintain effectiveness when abrupt degradation occur. As engine output deviations increase due to the progression of health degradation, it becomes difficult to distinguish the presence of faults from the health degradation through an observation of the engine outputs. As a result, an inflight diagnostic system loses its effectiveness. Accurate online estimation is critically important, Zou et al. designed a multi-time-scale observer to realize accurate online state estimation for some types of nonlinear singularly perturbed systems[4]. To ensure accurate online estimation, the FDI system must be able to resist in-flight abrupt degradation and sensor fault.

The performance of the proposed sensor FDI system based on an on-line OBEM update is evaluated by applying it to the engine model simulation.