Regression-Based Modeling of a Fleet of Gas Turbine Engines for Performance Trending

Module performance analysis is a well-established framework to assess changes in the health condition of the components of the engine gas-path. The primary material of the technique is the so-called vector of residuals, which are built as the difference between actual measurement taken in the gas-path and the values predicted by means of an engine model. Obviously, the quality of the assessment of the engine condition depends strongly on the accuracy of the engine model. The present paper proposes a new approach for data-driven modeling of a fleet of engines of a given type. Such black-box models can be designed by operators, such as airlines and third-party companies. The fleet-wide modeling process is formulated as a regression problem that provides a dedicated model for each engine in the fleet, while recognizing that all engines are of the same type. The methodology is applied to a virtual fleet of engines generated within the Propulsion Diagnostic Methodology Evaluation Strategy (ProDiMES) environment. The set of models is assessed quantitatively through the coefficient of determination and is further used to perform anomaly detection.

[1]  Jack D. Mattingly,et al.  Elements of Gas Turbine Propulsion , 1996 .

[2]  Michel L. Verbist,et al.  Experience With Gas Path Analysis for On-Wing Turbofan Condition Monitoring , 2013 .

[3]  Sébastien Borguet,et al.  A Sensor-Fault-Tolerant Diagnosis Tool Based on a Quadratic Programming Approach , 2008 .

[4]  Donald L. Simon,et al.  A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data , 2014 .

[5]  Ki-Young Jeong,et al.  Condition-Based Maintenance for Aircraft Engines , 2004 .

[6]  Wilfried P. J. Visser,et al.  GSP, a Generic Object-Oriented Gas Turbine Simulation Environment , 2000 .

[7]  Paul Fletcher,et al.  Gas Turbine Performance , 1998 .

[8]  Jerome Lacaille,et al.  Visual mining and statistics for a turbofan engine fleet , 2011, 2011 Aerospace Conference.

[9]  Stephen P. Boyd,et al.  Detecting Aircraft Performance Anomalies from Cruise Flight Data , 2010 .

[10]  Thomas M. Lavelle,et al.  A High-Fidelity Simulation of a Generic Commercial Aircraft Engine and Controller , 2010 .

[11]  Donald L. Simon,et al.  Aircraft Engine Gas Path Diagnostic Methods: Public Benchmarking Results , 2014 .

[12]  Stephen P. Boyd,et al.  Scalable Statistical Monitoring of Fleet Data , 2011, IFAC Proceedings Volumes.

[13]  Ranjan Ganguli,et al.  Jet Engine Health Signal Denoising Using Optimally Weighted Recursive Median Filters , 2010 .

[14]  Igor Loboda,et al.  Polynomials and Neural Networks for Gas Turbine Monitoring: A Comparative Study , 2010 .

[15]  James P. Herzog,et al.  High Performance Condition Monitoring of Aircraft Engines , 2005 .

[16]  David Rees,et al.  NONLINEAR GAS TURBINE MODELING USING FEEDFORWARD NEURAL NETWORKS , 2002 .

[17]  Michel Verleysen,et al.  Aircraft Engine Fleet Monitoring Using Self-Organizing Maps and Edit Distance , 2011, WSOM.

[18]  Igor Loboda,et al.  Diagnostic Analysis of Maintenance Data of a Gas Turbine for Driving an Electric Generator , 2009 .