An Integrated Approach for Aircraft Engine Performance Estimation and Fault Diagnostics

A Kalman filter-based approach for integrated on-line aircraft engine performance estimation and gas path fault diagnostics is presented. This technique is specifically designed for underdetermined estimation problems where there are more unknown system parameters representing deterioration and faults than available sensor measurements. A previously developed methodology is applied to optimally design a Kalman filter to estimate a vector of tuning parameters, appropriately sized to enable estimation. The estimated tuning parameters can then be transformed into a larger vector of health parameters representing system performance deterioration and fault effects. The results of this study show that basing fault isolation decisions solely on the estimated health parameter vector does not provide ideal results. Furthermore, expanding the number of the health parameters to address additional gas path faults causes a decrease in the estimation accuracy of those health parameters representative of turbomachinery performance deterioration. However, improved fault isolation performance is demonstrated through direct analysis of the estimated tuning parameters produced by the Kalman filter. This was found to provide equivalent or superior accuracy compared to the conventional fault isolation approach based on the analysis of sensed engine outputs, while simplifying online implementation requirements. Results from the application of these techniques to an aircraft engine simulation are presented and discussed.Copyright © 2012 by ASME

[1]  R. Luppold,et al.  Estimating in-flight engine performance variations using Kalman filter concepts , 1989 .

[2]  Nasa.  An Integrated Architecture for On-Board Aircraft: Engine Performance Trend Monitoring and Gas Path Fault Diagnostics , 2010 .

[3]  M. Sorum.  Estimating the Conditional Probability of Misclassification , 1971 .

[4]  Donald L. Simon,et al.  Enhanced Self Tuning On-Board Real-Time Model (eSTORM) for Aircraft Engine Performance Health Tracking , 2008 .

[5]  Yi-Guang Li,et al.  Performance-analysis-based gas turbine diagnostics: A review , 2002 .

[6]  Donald L. Simon,et al.  Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation , 2009 .

[7]  L. A. Urban.  Parameter Selection for Multiple Fault Diagnostics of Gas Turbine Engines , 1975 .

[8]  David L. Doel,et al.  TEMPER - A gas-path analysis tool for commercial jet engines , 1992 .

[9]  Ranjan Ganguli,et al.  Data Rectification and Detection of Trend Shifts in Jet Engine Path Measurements Using Median Filters and Fuzzy Logic , 2002 .

[10]  Olivier Léonard,et al.  A Sparse Estimation Approach to Fault Isolation , 2010 .

[11]  Donald L. Simon,et al.  Application of an Optimal Tuner Selection Approach for On-Board Self-Tuning Engine Models , 2011 .

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

[13]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[14]  Jonathan S. Litt,et al.  User's Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) , 2007 .

[15]  Aditya Kumar,et al.  Model-Based Fault Tolerant Control , 2013 .

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

[17]  Ranjan Ganguli,et al.  Trend Shift Detection in Jet Engine Gas Path Measurements Using Cascaded Recursive Median Filter With Gradient and Laplacian Edge Detector , 2004 .

[18]  Hans R. Depold,et al.  The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics , 1998 .