Evaluation of Aircraft Engine Gas Path Diagnostic Methods Through ProDiMES

Author:

Koskoletos Anastasios O.1,Aretakis Nikolaos2,Alexiou Alexios1,Romesis Christoforos1,Mathioudakis Konstantinos3

Affiliation:

1. Laboratory of Thermal Turbomachines, National Technical University, Athens 15710, Greece e-mail:

2. Assistant Professor Laboratory of Thermal Turbomachines, National Technical University, Athens 15710, Greece e-mail:

3. Professor Laboratory of Thermal Turbomachines, National Technical University, Athens 15710, Greece e-mail:

Abstract

Propulsion diagnostic method evaluation strategy (ProDiMES) offers an aircraft engine diagnostic benchmark problem where the performance of candidate diagnostic methods is evaluated while a fair comparison can be established. In the present paper, the performance evaluation of a number of gas turbine diagnostic methods using the ProDiMES software is presented. All diagnostic methods presented here were developed at the Laboratory of Thermal Turbomachinery of the National Technical University of Athens (LTT/NTUA). Component, sensor, and actuator fault scenarios that occur in a fleet of deteriorated twin-spool turbofan engines are considered. The performance of each diagnostic method is presented through the evaluation metrics introduced in the ProDiMES software. Remarks about each methods performance as well as the detectability and classification rates of each fault scenario are made.

Publisher

ASME International

Subject

Mechanical Engineering,Energy Engineering and Power Technology,Aerospace Engineering,Fuel Technology,Nuclear Energy and Engineering

Reference15 articles.

1. Gas Turbine Engine Health Management: Past, Present, and Future Trends;ASME J. Eng. Gas Turbines Power,2014

2. Aircraft Engine Gas Path Diagnostic Methods: Public Benchmarking Results;ASME J. Eng. Gas Turbines Power,2013

3. Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) User's Guide,2010

4. Turbofan Performance Deterioration Tracking Using Nonlinear Models and Optimization Techniques;J. Power Energy,2004

5. The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics;ASME J. Eng. Gas Turbines Power,1999

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