Robust Gas-Path Fault Diagnosis with Sliding Mode Applied in Aero-Engine Distributed Control System

Abstract

The technology of aero-engine gas-path fault diagnosis is an important way to improve flight safety and reliability and reduce maintenance costs. With the maturity of the new-generation engine distributed control system (DCS), uncertainties such as bus packet loss, time delay, and node function degradation have increasingly highlighted new challenges to engine fault diagnosis. At present, linear Kalman filter (LKF) is widely researched and used in engine fault detection and isolation (FDI), but its robustness has proved to be not strong. However, the sliding mode observer (SMO) is not only capable of fault reconstruction but also robust to system uncertainties and disturbances due to its unique discontinuous switching term, which tends to be an effective way to achieve robust fault diagnosis for aero engines and DCS with many uncertainties. This paper initially develops a distributed bus packaging model that supports time-delay and packet-loss simulating and timing planning based on SimEvents, providing a basis for the model-based design and verification. Then the SMO is adopted to design a robust gas-path diagnosis method for engine DCS, and the robust observing accuracy is improved by combining high-order sliding mode theory, LMI optimized observation matrix, and variable gain. The simulation results show the effectiveness and advantages in engine DCS application scenarios.