Coupling principal component analysis and Kalman filtering algorithms for on-line aircraft engine diagnostics

Abstract

Engine health monitoring has been an area of intensive research for more than three decades. Numerous methods have been developed with the goal of performing an accurate assessment of the engine condition. It is generally accepted that a practical implementation of a monitoring tool will rely on a combination of several techniques. In this framework, the present contribution proposes an original approach for coupling two diagnostic tools in order to enhance the capability of an engine health monitoring system. One tool is based on a principal component analysis scheme and the other is based on a Kalman filter technique. The three methodologies are compared and the benefit of the combined tool is demonstrated on simulated fault cases which can be expected in a commercial turbofan layout.