Diagnostic Methods for an Aircraft Engine Performance

The main gas path components, namely compressor and turbine, are inherently reliable but the operation of the aero engines under hostile environments, results into engine breakdowns and performance deterioration. Performance deterioration increases the operating cost, due to the reduction in thrust output and higher fuel consumption, and also increases the engine maintenance cost. In times when economic considerations dominate airline operators’ strategies, carrying out unnecessary rectification, can be very costly and time consuming. In an attempt to minimize such unexpected circumstances, having detailed knowledge prior to any inspection will allow the gas turbine user to take some of the maintenance action when it is necessary. Advanced engine-fault diagnostics tools offer the possibility of identifying degradation at the module level, determining the trends of these degradations during the usage of the engine, and planning the maintenance action ahead.

[1]  Jian-Guo Sun,et al.  Rough Set and Neural Network Based Fault Diagnosis for Aeroengine Gas Path , 2005 .

[2]  Xue Wei,et al.  Aircraft Engine Sensor Fault Diagnostics Based on Estimation of Engine's Health Degradation , 2009 .

[3]  Allan J. Volponi,et al.  Mathematical Methods of Relative Engine Performance Diagnostics , 1992 .

[4]  Graham C. Goodwin,et al.  Fault Detection and Diagnosis in Gas Turbines , 1991 .

[5]  Anastassios G. Stamatis,et al.  Setting up a Belief Network for Turbofan Diagnosis With the Aid of an Engine Performance Model , 2001 .

[6]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[7]  R. K. Agrawal,et al.  An Analysis Procedure for the Validation of On-Site Performance Measurements of Gas Turbines , 1979 .

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

[9]  M. J. Provost,et al.  COMPASS: A Generalized Ground-based Monitoring System , 1989 .

[10]  Ranjan Ganguli,et al.  Data Rectification and Detection of Trend Shifts in Jet Engine Gas Path Measurements Using Median Filters and Fuzzy Logic , 2001 .

[11]  T. Ross Fuzzy Logic with Engineering Applications , 1994 .

[12]  A. Tourlidakis,et al.  An expert system concept for diagnosis and monitoring of gas turbine combustion chambers , 2006 .

[13]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[14]  Jin Zhang,et al.  A PRACTICAL INTELLIGENT SYSTEM FOR CONDITION MONITORING AND FAULT DIAGNOSIS OF JET ENGINES , 1999 .

[15]  Pericles Pilidis,et al.  Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm , 2006 .

[16]  Mohsen Assadi,et al.  Development and multi-utility of an ANN model for an industrial gas turbine , 2009 .

[17]  Michael G. Dunn,et al.  INTERPRETATION OF GAS TURBINE RESPONSE DUE TO DUST INGESTION , 1987 .

[18]  Tong Seop Kim,et al.  Analysis of performance deterioration of a micro gas turbine and the use of neural network for predicting deteriorated component characteristics , 2008 .

[19]  Dan Simon,et al.  Constrained Kalman filtering via density function truncation for turbofan engine health estimation , 2010, Int. J. Syst. Sci..

[20]  Allan J. Volponi,et al.  Development of an Information Fusion System for Engine Diagnostics and Health Management , 2013 .

[21]  Meherwan P. Boyce,et al.  Modelling and Analysis of Gas Turbine Performance Deterioration , 1992 .

[22]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[23]  G. L. Merrington,et al.  Identification of dynamic characteristics for fault isolation purposes in a gas turbine using closed-loop measurements , 1988 .

[24]  Allan J. Volponi,et al.  The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study , 2000 .

[25]  Widen Tabakoff,et al.  Simulation of Compressor Performance Deterioration due to Erosion , 1989 .

[26]  Silvio Simani,et al.  Identification and fault diagnosis of a simulated model of an industrial gas turbine , 2005, IEEE Transactions on Industrial Informatics.

[27]  Ryszard Tadeusiewicz,et al.  Neural networks: A comprehensive foundation: by Simon HAYKIN; Macmillan College Publishing, New York, USA; IEEE Press, New York, USA; IEEE Computer Society Press, Los Alamitos, CA, USA; 1994; 696 pp.; $69–95; ISBN: 0-02-352761-7 , 1995 .

[28]  E. Applebaum Fuzzy classification for fault isolation in gas turbine engines , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[29]  Ihor S. Diakunchak Performance Deterioration in Industrial Gas Turbines , 1991 .

[30]  Raghunathan Rengaswamy,et al.  Design of sensor location based on various fault diagnostic observability and reliability criteria , 2000 .

[31]  Graeme L. Merrington,et al.  Fault diagnosis of gas turbine engines from transient data , 1989 .

[32]  R. Dyson,et al.  CF6-80 condition monitoring - The engine manufacturer's involvement in data acquisition and analysis , 1984 .

[33]  J. Celaya,et al.  Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation , 2012 .

[34]  K. Mathioudakis,et al.  Incorporating Neural Networks Into Gas Turbine Performance Diagnostics , 1997 .

[35]  David L. Doel Interpretation of Weighted-Least-Squares Gas Path Analysis Results , 2003 .

[36]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[37]  Marco Mucino,et al.  A DIAGNOSTIC SYSTEM FOR GAS TURBINES USING GPA-INDEX , 2005 .

[38]  Carl A. Palmer Combining Bayesian Belief Networks With Gas Path Analysis for Test Cell Diagnostics and Overhaul , 1998 .

[39]  Widen Tabakoff,et al.  Modeling of Compressor Performance Deterioration Due to Erosion , 1998 .

[40]  Allan J. Volponi Sensor Error Compensation in Engine Performance Diagnostics , 1994 .

[41]  Dimitri N. Mavris,et al.  A Fault Diagnosis Method for Industrial Gas Turbines Using Bayesian Data Analysis , 2010 .

[42]  Y. Li,et al.  The Impact of Measurement Noise in GPA Diagnostic Analysis of a Gas Turbine Engine , 2013 .

[43]  M. J. Provost,et al.  The use of optimal estimation techniques in the analysis of gas turbines , 1994 .

[44]  Riti Singh,et al.  Gas Turbine Engine and Sensor Fault Diagnosis Using Optimization Techniques , 2002 .

[45]  Stephen Ogaji,et al.  Gas Path Fault Diagnosis of a Turbofan Engine From Transient Data Using Artificial Neural Networks , 2003 .

[46]  M. Zedda Gas turbine engine and sensor fault diagnosis , 1999 .

[47]  Bruce A. Whitehead,et al.  Neural network approach to Space Shuttle Main Engine health monitoring , 1990 .

[48]  Eugene Kopytov,et al.  ЕXPERT SYSTEMS FOR EVALUATING THE AIRCRAFT POWER PLANTS' TECHNICAL СONDITION , 2010 .

[49]  Stephen Ogaji,et al.  Parameter selection for diagnosing a gas-turbine's performance-deterioration , 2002 .

[50]  Efstratios Ntantis Capability Expansion of Non-Linear Gas Path Analysis , 2014 .

[51]  K. Mathioudakis,et al.  Jet Engine Fault Detection with Discrete Operating Points Gas Path Analysis , 1991 .

[52]  K. Mathioudakis,et al.  Bayesian Network Approach for Gas Path Fault Diagnosis , 2004 .

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

[54]  Michele Pinelli,et al.  Artificial Intelligence for the Diagnostics of Gas Turbines—Part I: Neural Network Approach , 2007 .

[55]  Marco Mucino,et al.  CCGT performance simulation and diagnostics for operations optimisation and risk management , 2007 .

[56]  Qiang Shen,et al.  Reinforcing fuzzy rule-based diagnosis of turbomachines with case-based reasoning , 2008, Int. J. Knowl. Based Intell. Eng. Syst..

[57]  Jin Zhang,et al.  An Evaluation of Engine Faults Diagnostics Using Artificial Neural Networks , 2001 .

[58]  R. L. Elder,et al.  Experimental investigation of axial fan erosion and performance degradation , 2004 .

[59]  R. Kalman,et al.  New results in linear prediction and filtering theory Trans. AMSE , 1961 .

[60]  Pericles Pilidis,et al.  An Adaptation Approach for Gas Turbine Design-Point Performance Simulation , 2006 .

[61]  H. I. H. Saravanamuttoo,et al.  Predicting Gas Turbine Performance Degradation Due to Compressor Fouling Using Computer Simulation Techniques , 1989 .

[62]  P. C. Escher,et al.  Pythia: An object-orientated gas path analysis computer program for general applications , 1995 .

[63]  J. D. MacLeod,et al.  Implanted Component Faults and Their Effects on Gas Turbine Engine Performance , 1992 .

[64]  Chingiz Hajiyev,et al.  Fuzzy logic‐based automated engine health monitoring for commercial aircraft , 2008 .

[65]  Riti Singh,et al.  Advanced engine diagnostics using artificial neural networks , 2003, Appl. Soft Comput..

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

[67]  Donald L. Simon,et al.  Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics , 2003 .

[68]  G. R. Lazalier,et al.  A Gas Path Performance Diagnostic System to Reduce J75-P-17 Engine Overhaul Costs , 1978 .

[69]  David Doel,et al.  JET-X: jet engine troubleshooting expert system , 1988, Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications.

[70]  A. G. Stamatis,et al.  Evaluation of gas path analysis methods for gas turbine diagnosis , 2011 .

[71]  Douglas Probert,et al.  Prospects for aero gas-turbine diagnostics: a review , 2004 .

[72]  Xin Li,et al.  An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation , 2008 .

[73]  K. Mathioudakis,et al.  Enhanced Fault Localization Using Probabilistic Fusion With Gas Path Analysis Algorithms , 2008 .

[74]  Stephen Ogaji,et al.  Engine-fault diagnostics:an optimisation procedure , 2002 .