1. TURBOFAN ENGINE HEALTH ASSESSMENT FROM FLIGHT DATA Proceedings of ASME Turbo Expo 2014, June 16 – 20, 2014, Düsseldorf, Germany, GT2014-26443

2. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data2 Contents  Introduction Scope of the paper Test case description  Measurement analysis for diagnostic purposes  Engine adaptive model The PROOSIS platform Model creation process  Diagnostic methods application Data pre-processing The Probabilistic Neural Network (PNN) The Deterioration Tracking Method  Summary-Conclusions

3. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data3 Contents  Introduction Scope of the paper Test case description  Measurement analysis for diagnostic purposes  Engine adaptive model The PROOSIS platform Model creation process  Diagnostic methods application Data pre-processing The Probabilistic Neural Network (PNN) The Deterioration Tracking Method  Summary-Conclusions

4. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data4  Over the years various GPA diagnostic methods have been proposed, ranging from simple trend analysis-based methods to more advanced model-based methods.  Although the potential of many of these methods has been demonstrated, real-world applications from on-wing data are sparse.  The efficiency of diagnostic methods, as a function to their complexity and/or the accuracy of the available models is also under question.  This work presents the application of several approaches -where engine model of different detail are considered- to engine health assessment, using on-wing data obtained from an aircraft engine. Scope of the paper

5. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data5 Test Case Description High bypass ratio turbofan engine of a commercial short-range aircraft On-wing engine performance monitoring system provides snapshot data for T/O and MCR operating points Altitude Time 1100 cycles ~ 1 year of operation P2 P125 P25 P3 P49 T2 T25 T3 EGT N1 N2 Wf Cycles ACC pos. SVA pos. Focus on MCR data, since: significantly more populated absolute humidity is negligible closer to quasi-steady state ALT [km]11 MN0.78 OPR28.5 BPR5.7 FN [kN]19.5 SFC [g/(kN·s)]16.9

6. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data6 Test Case Description Breakdown of test case engine operational environment The regions an engine operates have significant effect on its performance degradation Engines operating by more than 70% through category C airports are expected to have lower degradation rates compared with operation through other regions CategoryAirports ADust producing regions BRegions with significant industrial pollution and chemicals CCommon regions, not classified as A or B

7. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data7 Contents  Introduction Scope of the paper Test case description  Measurement analysis for diagnostic purposes  Engine adaptive model The PROOSIS platform Model creation process  Diagnostic methods application Data pre-processing The Probabilistic Neural Network (PNN) The Deterioration Tracking Method  Summary-Conclusions

8. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data8 Measurement analysis for diagnostic purposes EGT variation with engine cycles (Raw Data) T2=259 K T2=240 K T2=245 K

9. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data9 Measurement analysis for diagnostic purposes Raw and corrected EGT variation with engine cycles

10. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data10 Measurement analysis for diagnostic purposes Operating conditions effect is diminished by using the changes of measurements against their reference value ‘Deltas’ of measurements Yis the measured quantity Y0Y0 is the reference value of Y measured quantity 10K

11. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data11 Measurement analysis for diagnostic purposes Corrected EGT variation with engine cycles

12. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data12 Measurement analysis for diagnostic purposes Corrected ΔEGT variation with engine cycles shift 1 shift 2 shift 3

13. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data13 Measurement analysis for diagnostic purposes Corrected ΔWf variation with engine cycles

14. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data14 Measurement analysis for diagnostic purposes Corrected ΔP3 variation with engine cycles

15. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data15 Measurement analysis for diagnostic purposes Corrected ΔN2 variation with engine cycles

16. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data16 Measurement analysis for diagnostic purposes No observable shifts exist in the T/O ΔEGT or other parameters

17. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data17  Limited diagnostic information can be extracted from raw and corrected measurements.  Parameter deltas analysis, gives an indication of engine performance shifts  For sudden shift detection, the use of more advanced diagnostic methods may not be necessary  Concerning deterioration, a clear trend is observable in all measurements. Measurement analysis for diagnostic purposes Diagnostic Verdict

18. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data18 Measurement analysis for diagnostic purposes Diagnostic Verdict  EGT increase coincides with Wf increase and P3, N2 decrease. A simultaneous fault in all sensors is highly unlikely to occur.  No observable shifts in T/O ΔEGT means fault is related to CR operation.  The occurrence of two consecutive shifts indicates that this is probably not a permanent internal gas-path component fault.  Although a potential component fault is detectable, neither the cause of performance shifts can be identified nor information about the deteriorated components can be determined. This information is crucial for planning correcting actions such as engine inspection or compressor washing and applying prognostic methods.

19. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data19 Contents  Introduction Scope of the paper Test case description  Measurement analysis for diagnostic purposes  Engine adaptive model The PROOSIS platform Model creation process  Diagnostic methods application Data pre-processing The Probabilistic Neural Network (PNN) The Deterioration Tracking Method  Summary-Conclusions

20. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data20 Engine Adaptive Model The PROOSIS platform  Object-Oriented  Steady State  Transient  Mixed-Fidelity  Multi-Disciplinary  Distributed  Multi-point Design  Off-Design  Test Analysis  Diagnostics  Sensitivity  Optimisation  Deck Generation  Connection with Excel & Matlab  Integration of FORTRAN, C, C++

21. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data21 The PROOSIS platform TURBO library of gas turbine components  Industry-accepted performance modelling techniques  Respects international standards in nomenclature, interface & OO programming Compressor map Turbine map Fan map

22. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data22 Engine Adaptive Model Model creation process

23. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data23 Automatic Deck Generation Procedure Robust Mathematical Model Generic model adaptation procedure Customer Deck Generation DP & OD Performance Data

24. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data24 Engine Adaptive Model Predictions lie within: ±2.0% for the ICAO model ±0.5% for the ADAPTED model Two performance models considered:  ICAO  ICAO – generic model based on information available in the ICAO aircraft engine emissions data bank ADAPTED  ADAPTED – engine specific model using additional off-design points from the first 50 cycles.

25. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data25 Contents  Introduction Scope of the paper Test case description  Measurement analysis for diagnostic purposes  Engine adaptive model The PROOSIS platform Model creation process  Diagnostic methods application Data pre-processing The Probabilistic Neural Network (PNN) The Deterioration Tracking Method  Summary-Conclusions

26. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data26 Data pre-processing Measurement deltas smoothing procedure involves Measurement deltas smoothing procedure involves: 1.exponential moving average to reveal step changes and/or sudden shifts. 2.these regions are excluded and a best fit polynomial is applied to the remaining data. 3.smoothed deltas are formed from the combination of the detected step changes, if any, and the best fit line

27. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data27 Diagnostic Methods Application Two model-based diagnostic methods are applied:  Probabilistic Neural Network (PNN)  The Probabilistic Neural Network (PNN) method – a classification method allowing diagnosis of faulty or deteriorated engine components Deterioration Tracking  The Deterioration Tracking method – that allows estimation of health parameters deviation, through an appropriate optimization approach. ComponentHealth ParameterSymbol LP CompressorFlow factorSW2 Efficiency factorSE2 HP CompressorFlow factorSW25 Efficiency factorSE25 HP TurbineFlow factorSW4 Efficiency factorSE4 LP TurbineFlow factorSW45 Efficiency factorSE45 Set of components health parameters used for diagnosis

28. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data28 The Probabilistic Neural Network method PNN architecture Three layer feed-forward network allowing statistical pattern recognition based on Bayes’ decision rule

29. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data29 The Probabilistic Neural Network method Considered engine health conditions Considered Health Condition SymbolDeviated ParametersNo. of Training patterns LP Compressor faultLPCSW2, SE2 12 HP Compressor faultHPCSW25, SE25 12 HP Turbine faultHPTSW4, SE4 21 LP Turbine faultLPTSW45, SE45 21 Compressor faultCSW2, SE2, SW25, SE25 9 Turbine faultTSW4, SE4, SW45, SE45 18 HP system faultHPSW25, SE25, SW4, SE4 18 LP system faultLPSW2, SE2, SW45, SE45 18 Healthy operationOKNONE 36 Total no. of training patterns: 165

30. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data30 PNN estimated probabilities Estimated probabilities are exhaustive and mutually exclusive, among considered classes

31. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data31 The Probabilistic Neural Network method PNN results using the ADAPTED model A fault located in HPT is detected in all points but two regions

32. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data32 The Probabilistic Neural Network method PNN results using ICAO model The HPT fault is detected with lower probabilities

33. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data33 The deterioration tracking method Main advantages of this method:  Applicable in the case of limited number of available measurements  Robustness against high levels of measurement scattering and noise. Y g from Engine Performance Model Calculate OF Output f Choose new f Y f from previous step Yes No OptimizationAlgorithm Is OF minimum? f

34. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data34 The deterioration tracking method Estimated deviations of HPT efficiency

35. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data35 The deterioration tracking method Estimated deviations of HPT flow capacity

36. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data36 The deterioration tracking method Estimated deviations of HPC flow capacity

37. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data37 Diagnostic Methods Application Diagnostic Verdict  The fault is connected with the HP turbine system, according to the findings of both PNN and the Deterioration Tracking methods (when using the engine specific model).  The effect of the fault on the component efficiency is dominant.  The fault does not concern degradation of a turbo machinery internal gas-path component such as due to erosion, fouling or wear, but a fault that can be intermittent. From the above it can be concluded that the fault is probably connected to an HP turbine sub-system.

38. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data38 Diagnostic Methods Application The specific engine is equipped with ACC A failure of the bleed valve is expected to cause increased tip clearances, thus decreasing HP turbine efficiency, as detected by the diagnostic methods Active Clearance Control (ACC) subsystem fault

39. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data39 Contents  Introduction Scope of the paper Test case description  Measurement analysis for diagnostic purposes  Engine adaptive model The PROOSIS platform Model creation process  Diagnostic methods application Data pre-processing The Probabilistic Neural Network (PNN) The Deterioration Tracking Method  Summary-Conclusions

40. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data40 Summary  On-wing data obtained over a year from an engine of a commercial short-range aircraft have been analyzed using different approaches.  First, a trend analysis was performed using a measurements-derived model leading to the identification of engine deterioration and an engine fault.  Next, a diagnostic process consisting of two advanced model-based diagnostic methods has been applied.  Engine performance models of different adaptation quality have been developed using a semi-automated adaptation procedure.  Both PNN and the deterioration tracking method identify HP turbine as the faulty component.  Since performance shifts are detectable only in mid-cruise data, the fault is attributed to ACC operation, a finding confirmed from the recordings of the ACC valve position.

41. GT2014- 26443 Turbofan Engine Health Assessment From Flight Data41 Conclusions  Using a typical set of on-wing measurements, valuable diagnostic information can be obtained.  Trend analysis is sufficient to detect deterioration and sudden changes in operation e.g. performance shifts.  Advanced diagnostic methods can effectively identify and quantify engine deterioration and component faults.  This information can further be used to support a decision making procedure e.g. regarding safety or maintenance planning.  Using a generic rather an engine specific adapted model affects the quality of the diagnostic information obtained.

42. TURBOFAN ENGINE HEALTH ASSESSMENT FROM FLIGHT DATA Proceedings of ASME Turbo Expo 2014, June 16 – 20, 2014, Düsseldorf, Germany, GT2014-26443