Robust In-Flight Sensor Fault Diagnostics for Aircraft Engine Based on Sliding Mode Observers

For a sensor fault diagnostic system of aircraft engines, the health performance degradation is an inevitable interference that cannot be neglected. To address this issue, this paper investigates an integrated on-line sensor fault diagnostic scheme for a commercial aircraft engine based on a sliding mode observer (SMO). In this approach, one sliding mode observer is designed for engine health performance tracking, and another for sensor fault reconstruction. Both observers are employed in in-flight applications. The results of the former SMO are analyzed for post-flight updating the baseline model of the latter. This idea is practical and feasible since the updating process does not require the algorithm to be regulated or redesigned, so that ground-based intervention is avoided, and the update process is implemented in an economical and efficient way. With this setup, the robustness of the proposed scheme to the health degradation is much enhanced and the latter SMO is able to fulfill sensor fault reconstruction over the course of the engine life. The proposed sensor fault diagnostic system is applied to a nonlinear simulation of a commercial aircraft engine, and its effectiveness is evaluated in several fault scenarios.

[1]  Donald L. Simon,et al.  An Integrated Architecture for On-Board Aircraft Engine Performance Trend Monitoring and Gas Path Fault Diagnostics , 2010 .

[2]  Halim Alwi,et al.  Development and application of sliding mode LPV fault reconstruction schemes for the ADDSAFE Benchmark , 2014 .

[3]  Christopher Edwards,et al.  Sliding mode control : theory and applications , 1998 .

[4]  Donald L. Simon,et al.  Hybrid Kalman Filter: A New Approach for Aircraft Engine In-Flight Diagnostics , 2013 .

[5]  Halim Alwi,et al.  Sliding Modes for Fault Detection and Fault Tolerant Control , 2011 .

[6]  Jaime A. Moreno,et al.  Strict Lyapunov Functions for the Super-Twisting Algorithm , 2012, IEEE Transactions on Automatic Control.

[7]  Donald L. Simon,et al.  Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics , 2005 .

[8]  Nader Meskin,et al.  Adaptive sliding mode observer for sensor fault diagnosis of an industrial gas turbine , 2015 .

[9]  Halim Alwi,et al.  Robust fault reconstruction for linear parameter varying systems using sliding mode observers , 2014 .

[10]  Feng Lu,et al.  Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach , 2013 .

[11]  Donald L. Simon,et al.  Implementation of an Integrated On-Board Aircraft Engine Diagnostic Architecture , 2011 .

[12]  Feng Lu,et al.  Gas-Path Health Estimation for an Aircraft Engine Based on a Sliding Mode Observer , 2016 .

[13]  Walter C. Merrill,et al.  Advanced detection, isolation and accommodation of sensor failures: Real-time evaluation , 1988 .

[14]  Chee Pin Tan,et al.  A robust sensor fault reconstruction scheme using sliding mode observers applied to a nonlinear aero-engine model , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).