Towards an integrated development of PHM systems for aircraft engines: In-design selection and validation of health indicators

This paper proposes an approach for the in-design selection and validation of health indicators (HI), based on a virtual prototype, which is part of a larger development scheme named integrated development of PHM. Physics-based modeling is combined with sensitivity analysis to select the most relevant HIs and then with uncertainty propagation to estimate the probability density functions of health indicators for both healthy and degraded states. The validation is then performed through the computation of original performance indicators. The methodology is finally applied to the selection and validation of health indicators for an aircraft fuel system and demonstrates its interest by assessing the performance that the user can expect in terms of detection, identification and localization.

[1]  P.W. Kalgren,et al.  Defining PHM, A Lexical Evolution of Maintenance and Logistics , 2006, 2006 IEEE Autotestcon.

[2]  Tarantola Stefano,et al.  Uncertainty in Industrial Practice - A Guide to Quantitative Uncertainty Management , 2008 .

[3]  Carl S. Byington,et al.  Prognostics and Health Management Software for Gas Turbine Engine Bearings , 2007 .

[4]  Carl S. Byington,et al.  An Overview of Selected Prognostic Technologies With Application to Engine Health Management , 2006 .

[5]  Rolf Isermann,et al.  Supervision, fault-detection and fault-diagnosis methods — An introduction , 1997 .

[6]  Mohamed Ben-Daya,et al.  Handbook of maintenance management and engineering , 2009 .

[7]  N. Mechbal,et al.  Methodology for the diagnosis of hydromechanical actuation loops in aircraft engines , 2012, 2012 20th Mediterranean Conference on Control & Automation (MED).

[8]  Krishna R. Pattipati,et al.  A Review of Diagnostic Techniques for ISHM Applications , 2005 .

[9]  Bertrand Iooss Revue sur l’analyse de sensibilité globale de modèles numériques , 2011 .

[10]  Yongming Liu,et al.  Bayesian Analysis for Fatigue Damage Prognostics and Remaining Useful Life Prediction , 2016 .

[11]  Jean-Remi Masse,et al.  Numerical Key Performance Indicators for the Validation of Phm Health Indicators with Application to a Hydraulic Actuation System , 2013 .

[12]  Pierre Beauseroy,et al.  System Phm Algorithm Maturation , 2013 .

[13]  Piero Baraldi,et al.  Ensemble of bootstrapped models for the prediction of the remaining useful life of a creeping turbine blade , 2012, 2012 IEEE Conference on Prognostics and Health Management.

[14]  Benjamin Lamoureux,et al.  A combined sensitivity analysis and kriging surrogate modeling for early validation of health indicators , 2014, Reliab. Eng. Syst. Saf..

[15]  B. Saha,et al.  Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques , 2008, 2008 IEEE Aerospace Conference.

[16]  Sankalita Saha,et al.  Prognostic Performance Metrics , 2011 .

[17]  Emanuele Borgonovo,et al.  Global sensitivity measures from given data , 2013, Eur. J. Oper. Res..

[18]  Jean-Remi Masse,et al.  An approach to the health monitoring of the fuel system of a turbofan , 2012, 2012 IEEE Conference on Prognostics and Health Management.

[19]  M. Pecht,et al.  Failure mechanisms based prognostics , 2008, 2008 International Conference on Prognostics and Health Management.

[20]  Nazih Mechbal,et al.  Improving Aircraft Engines Prognostics and Health Management via Anticipated Model-Based Validation of Health Indicators , 2014 .

[21]  Emanuele Borgonovo,et al.  A new uncertainty importance measure , 2007, Reliab. Eng. Syst. Saf..

[22]  N. Metropolis,et al.  The Monte Carlo method. , 1949 .