Engine Health Monitoring Demonstrator based on GPA: A Gas Path Analysis GPA concept developed using the Gas turbine Simulation Program (GSP)

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Integrated Project – Level 2 Aeronautics and Air Transport

Title of the deliverable:

Engine Health Monitoring

Demonstrator based on GPA

A Gas Path Analysis GPA concept developed using the Gas turbine Simulation Program (GSP)

Prepared by: Wilfried Visser & Adam Head, Delft University of Technology

Document control data

Deliverable No. : D4.2.7

Revision: 2.0 Date of issue : April 2014

Author’s name: W. Visser & A. Head Status: Released WP Leader´s name: Pavla Sztwiertniova Status: Approved SP Coordinator´s name: Vladimír Opluštil Status: Approved

Release status: CO

Start Date of ESPOSA: 1st _{October 2011 Duration: 48 months}

This document has been produced by the ESPOSA consortium under FP7 of the EU. Copyright and all other rights are reserved by the partners in the ESPOSA consortium.

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Summary

This report describes the work conducted by TUD for WP 4.2.7 of the ESPOSA project for the development of Gas Path Analysis (GPA) based Engine Health Monitoring (EHM) concepts for the BE1 and BE2 engine designs. Models developed with the Gas turbine Simulation Program GSP have been used for the development and for the simulation of deteriorated engine data.

A comprehensive differential GPA concept is demonstrated and feasibility assessed assuming sufficient sensor data can be obtained from the BE1 and BE2 engines during operation. The concept is based on the Adaptive Modeling capability of GSP. With differential GPA, engine condition can be determined on the component level, offering significant potential to optimize the maintenance concept and minimize maintenance costs. However, in the current design of the BE1 and BE2 engines, less than sufficient data are measured. Therefore differential GPA can only be applied after more sensors are added which in turn means extra costs. At this stage, the trade-off between extra costs and benefits cannot be made. For small, low cost engines like the BE1 and BE2, additional sensors are relatively expensive.

As a consequence, a parametric performance monitoring GPA concept has been developed and demonstrated instead, still offering significant benefits for the maintenance concept. Baseline functions have been defined for reference engine performance to which operational data can be compared. From the deviations conclusions can be drawn w.r.t. engine condition and to a limited extent also gas path component condition.

The GSP project for demonstrating differential GPA and the performance monitoring concept is provided separately in digital format with the file Eposa_D1331_BE1_GPA_rev2.0.mxl.

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Acronyms

Abbreviation Description

BE1 (BE2) Engine type designation; Basic Engine 1 (2)

c Flow velocity [m/s] DP Design point GPA Gas Path Analysis

GSP Gas turbine Simulation Program H Flight altitude [m]

HP High Pressure

Hv Fuel lower heating value [MJ/kg] ISA International Standard Atmosphere ITT Inter turbine temperature o_{C (= Tt4)}

N, N% Rotor speed, % rotor speed

NASA National Aeronautics and Space Administration

CIAM Central Institute of Aviation Motors

OD Off-design

P, Pt Pressure, Total pressure

PBS PBS Velká Bíteš, Czech Republic FOD Foreign Object Damage

PR Pressure Ratio PW Power [kW]

SFC Specific fuel consumption SL Sea Level

T, Tt Temperature, Total temperature

V Air speed [km/h] W Mass flow [kg/s[

Wc Corrected mass flow (W*sqrt(theta)/delta) [kg/s] WF Fuel flow

WP Work Package

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Indices

Abbreviation Description b Burner or combustor c Compressor gg Gas generator pt Power turbine

tgg Gas generator or HP turbine c Corrected

I isentropic

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1 Introduction

The present report is a deliverable for the SP4/WP4.2 subproject ‘Smart health monitoring system’ of the ESPOSA project rewarded in the fourth call of the 7th Framework Program of the EU. The report describes the development of a gas path analysis concept for the BE1 and BE2 engines. The Gas turbine Simulation Program GSP is used with the GSP models developed under WP1.3 (references [4][5][6][7]) to generate simulation deteriorated performance data. The GSP Adaptive modeling functionality developed at Delft University of Technology is used to demonstrate a differential gas path GPA analysis method, assuming sufficient performance data are measured. With differential GPA, engine condition can be monitored on the component level, offering significant benefits for the maintenance concept in terms of costs, availability, reliability and safety. Since the actual set of measured data may be less which in turn leads to differential GPA not being feasible, an alternative performance monitoring and trending concept is developed and demonstrated, capable of monitoring overall engine gas path condition.

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2 The Gas turbine Simulation Program GSP

The Gas turbine Simulation Program, GSP [1] is a 0-D component based modeling environment developed by Dutch National Aerospace Laboratory NLR and Delft University of Technology. GSP's flexible object-oriented architecture allows steady state and transient simulation of virtually any gas turbine configuration using a user-friendly drag & drop interface. Gas turbine engines models can be rapidly prepared in order to perform various analyses. These include performance prediction, control system performance analysis/optimization, diagnostics/prognostics, failure analysis, structural and thermal load prediction and life prediction.

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3 GSP BE1 turboprop engine model

3.1. General

The engine model is to be used for advanced steady-state and transient performance prediction purposes. In particular:

 effect studies for cycle optimization (sensitivity analysis) by the OEM,

 fuel step transient simulations for control system development (system identification),

 development and demonstration of gas path analysis concepts (Esposa WP 4.2) 3.2. GSP model configuration

In Figure 2 a cross section drawing of the BE1 engine is given. Note that old station numbering standard is used (1 = compressor inlet, 3 is HP turbine inlet etc.).

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The BE1 engine is a turboprop engine. This means both shaft power and, depending on the exhaust nozzle configuration, some jet thrust is provided. In GSP this means the power turbine load is specified, taking power from the gas and leaving gas at some pressure level above ambient to expand into a jet. Jet thrust will only be effective for forward thrust of vertical lift to the extent that the jet will be directed backwards or downwards. This aspect however remains outside the scope of the engine performance simulations (but is important for aircraft performance aspects).

Figure 3 – BE1 GSP model configuration

Figure 3 shows the BE1 model in the GSP modeling environment corresponding to the turboprop cycle configuration. The icons numbered 10 to 16 (the number is depicted in the top right corner of the component block) represent the primary gas path engine components. The gas generator consists of an inlet (10), a compressor (11), a combustor (13) and the high pressure turbine (14). The gas generator exit gas is expanded in the low pressure power turbine (15). In the exhaust nozzle (16) the power turbine exit gas which still has some over pressure is expanded into a jet at the nozzle exit station 9, providing some thrust. Since there is no divergent nozzle part nozzle throat station 8 is equivalent to exit station 9 in the GSP model. The next step is to configure the component models by double clicking the icons after which component data and performance characteristics can be specified corresponding to design/reference or measurement data.

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4 GSP Adaptive Modeling Gas path analysis

The GSP Adaptive Modeling (AM) functionality, developed at Delft University of Technology, is used to demonstrate the differential gas path analysis (GPA) concept for the BE1 and BE2 engines. Details of the method are described in [2] and [3]. Below follows a summarized description of the method.

An off-design operating point in GSP is a solution of a set of non-differential equations. A number of ‘unknowns’ or states equal to the number of equations represent a set of performance parameter that uniquely defines the engine operating point. The differential equations are evaluated using thermodynamic calculations, algorithms and table lookups, resulting in the high degree of non-linearity.

The generic adaptive modeling method is an extension to the set of equations. A number of simple equations are added requiring certain parameters to be equal to measured values. An equal number of unknown condition parameter variables have to be added to ‘allow’ the engine model to ‘adapt’ to the measured data by changing condition deltas such as turbomachinery isentropic efficiencies and flow capacities. In equation (1) the complete set of equations for an adaptive model is shown. The upper left section represents the reference engine: f1 through fn are the n error equations based on the conservation laws with the unknown states through . ε represents the relative equation tolerance (convergence criterion for the conservation equations) and should be very close to zero (typically 0.0001). fm1 through fmm

represent the m additional equations added in adaptive modelling mode and simply require a model output parameter to be equal to a specified measurement value. sc1 through scm are the scalars representing the unknown condition factors that need to be solved for. εm1 through εmm represent the separate tolerances for the adaptation to the measurement parameters.

 

mm

 

n mm

 

c mm

 

cm mm m m m m c m c m n m m m c n c n n n n cm c n s f s f s f s f s f s f s f s f s f s f s f s f s f s f s f s f                                     1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 (1)

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(2) with S̅ including both the s and sc elements and ͞ε including elements equal to the conservation equation tolerance ε and measurement tolerances εm. The measurement equations representing the adaptation constraints for a measurement i are

f_m_i  P_i_mdl P_i_meas _m_i (3) with Pi mdl and Pi meas the adapted and measured values of parameter Pi respectively. The GSP Newton-Raphson based solver is used to iterate towards the solution, simultaneously with the off-design simulation, and at this stage there are no further numerical additions required. The absence of outside iteration loops provides optimal stability and minimal complexity.

4.1. Reference

models

The objective is to extend an existing gas turbine model with an adaptive modelling capability, without having to interfere with the model itself. In GSP, this can be simply done by drag-and-drop of the adaptive model control component icon (top-left in Figure 4). With adaptive mode turned off, the model represents the reference or baseline engine.

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The reference model must be tuned to performance data in order to obtain an accurate baseline. This means matching the model design point to a specific engine operating point data set, usually at high power levels at standard conditions such as maximum take-off thrust for a turbofan engine. If necessary, model parameters can be further fine-tuned to improve the match with available off-design data. Even if component maps are not available and must be scaled from similar public domain maps, errors can be kept small as long as the operating point stays close the design point. This is the case for example with gas path analysis diagnostics on maximum take-off power engine pass-off tests at KLM engine overhaul facility Amsterdam (see Case study section).

The accuracy of the reference model match affects the adaptive model simulation process. Reference engine model errors will interfere with the adaptive modelling numerical solution. Therefore, ‘calibration factors’ fc are introduced, compensating the adaptive model numerics for model errors. Equation (4) shows how a model parameter Pi_mdl_raw is calibrated using the ratio of the design point measured and model parameter values.

des mdl i des meas i i c i c raw mdl i mdl i P P f f P P   . (4)

Normally, if an accurate design point match has been obtained, the fc factors are very close to 1. The fc factor calibration method was found to have a significant effect on adaptive model stability and results, even if the fc factors only deviated 1% from unity. Another important consideration is the source of the data for tuning the reference engine model design point. This source optimally is the same engine test bed under the same calibration settings. Data from a single engine test, well corresponding to the average (new or overhauled) engine performance are usually sufficient to be used for an entire engine fleet. Even better results would be obtained if a range of engine tests would be used and averaged to eliminate measurement scatter effects. Ultimately, the best but also most laborious approach would be to match models to every new engine at the start of its service life (or time between overhaul) and keep

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the model for diagnostics for the particular engine. Then engine to engine variation is eliminated and performance deviations will be due to deterioration or faults only. This approach may well be applied in the future in on-wing or remote-wireless continuous engine monitoring systems. With the adaptive model continuously running on-board in the FADEC, more interesting opportunities emerge, such as adaptive control logic.

4.2. Selection of parameters

The ‘square’ equation set with an equal number of measurements and conditions parameters is the most straightforward approach. However, the number of condition parameters and especially measurements may vary among engine types and applications. Ideally, a large number of accurate measurements is available, covering the gas path conditions at most engine stations, and exceeding the number of condition parameters. In that case, the solution of the ‘over-determined’ equation set would be a minimization problem. In most cases however, the number of measurements is limited and often smaller than the number of condition parameters required for a complete representation of engine health including all deterioration and failure modes. Several solutions have been proposed to handle the case of fewer measurements than condition parameters [3].

Different sets can be used to identify different fault- or deterioration cases. With an adaptive modelling tool that can be rapidly configured, this approach is attractive and therefore has been used in the GSP diagnostics module.

4.3. Measurement

uncertainty

The measurement tolerances εm1 through εmm are independent of conservation law inaccuracy ε and represent measurement specific tolerances for the adaptation equations. The εm values are separately user specified corresponding to measurement uncertainty data. Normally, the εm values will be larger than ε and can be tuned to obtain optimal results.

With large εm values, solutions may be found at the extremes of εm margins, which are unrealistic in a sense that the deviation from the reference engine parameter value is ‘ignored’ by the solution. In the future, additional methods may be applied to account for statistical probability distribution of measurement error using weight

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factors for example. This will allow better representation of measurement error and provide solutions with maximum probability with larger measurement uncertainty margins.

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5 GSP Differential Gas Path Analysis (GPA)

5.1. Preparing the engine model

The BE1 and BE2 engine configurations are very similar in design and thus have the same configuration in the GSP environment. This means the gas path analysis concept will be identical with identical performance sensor sets. Therefore the differential Gas Path Analysis (GPA) method is demonstrated here using the BE1 engine model and is directly applicable to the BE2 engine.

The first step is to determine the different condition parameters that will accurately describe the deterioration of the engine. Using GPA within the context of the problem at hand only deterioration in the compressor and turbines is taken into account. This deterioration can be found in two separate parameters, the first is the isentropic efficiency ηis and the other is corrected mass flow wc. In the case of the BE1 engine there are therefore 6 condition parameters that can be applied. As explained in section 2.2 in order to perform gas path analysis the model needs to be expanded with a number of measurement parameters for equally many condition parameters. Since there are as much as 6 applicable condition parameters (3x2), there will have to be 6 independent parameters measured. How the concept would work in this ideal case is more or less described in [2].

However, in the actual BE1 engine there are only 5 parameters measured that can be used for this purpose [9]; namely gas generator rotor speed Ngg, power

turbine rotor speed Npt, power turbine exhaust temperature , fuel flow wf (assuming fuel pressure can be translated to fuel flow), and power turbine torque TRQpt. Two of these will have to be ignored, namely the Ngg and Npt. The first cannot be used since this is the control input parameter of the engine under investigation. The second cannot be used since the propeller and therefore the power turbine run at constant speed. The value for Npt will therefore also remain constant. This means that there are only 3 measurement parameters available for gas path analysis.

Therefore it is impossible to test all 6 condition parameters simultaneously. Therefore a practical way to overcome this problem of too few performance measurement parameters is to take a number of different subsets of 6 condition parameters which is represented in different cases, Table 1. This concept is

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described in [3]. Different combinations of measurement and condition parameters are used to isolate the responsible deteriorated component. If the wrong condition parameter is chosen in the subset (different cases) then we see nonrealistic results that can be neglected. The approach used to solving this is by first solving for the three parameter, and then for the three wc parameters. There are no field or test rig performance data available for the BE1 and BE2 engines. Therefore it is not possible to obtain the necessary measurements at this time. In order to still be able to test GPA on this model, deteriorated engine performance data will have to be simulated using GSP’s function to model deterioration.

Several deterioration cases will be treated in this chapter. For these

deteriorations a single operating point of the engine is chosen. This operating point is the model design point: 100% Nc_c at sea level static conditions. This point is

chosen because at the model design point, all the parameters are a close match. Also this is most likely the condition that will be used for test cell testing. A full overview of the different cases and their respective deterioration values are given in Table 1. In each of these cases deterioration will be applied to a subset of the 6 condition parameters, which is indicated by a percentage in the column for that condition parameter. A total of 21 cases will be investigated for GPA purposes with the majority contained in appendix A. As can be seen in this table the cases can be divided into 3 groups, namely a group with only deterioration (cases 1-7), a group with both efficiency ηis and deterioration (cases 8-14), and a group with only deterioration (cases 15-21).

5.2. Applying adaptive modeling to the BE1

Table 1 Simulated condition parameter deterioration for GPA investigation

Case 1 -5% 0% 0% 0% 0% 0% Case 2 0% 0% -5% 0% 0% 0% Case 3 0% 0% 0% 0% -5% 0% Case 4 -5% 0% -5% 0% 0% 0% Case 5 -5% 0% 0% 0% -5% 0% Case 6 0% 0% -5% 0% -5% 0% Case 7 -5% 0% -5% 0% -5% 0% Case 8 -5% -5% 0% 0% 0% 0% Case 9 0% 0% -5% -5% 0% 0%

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5.3. GPA

Results

This section contains some results from the analysis of the different cases. The results for each case consist of two parts. The first part is the result if only the

condition parameters are taken into account and the second part is for taking only into account. Not all cases will be treated in this section. Detailed results for all the cases can be found in appendix A.

Only deterioration (Cases 1-7)

The first group of cases includes various subsets of deterioration in the condition parameter only. This section will elaborate on a few of those cases. The first case investigated is case 1. This is the case where the simulated deterioration is -5% . The graphs for this case are shown in Figure 5 and Figure 6. If is the only condition parameter selected it is clear from the graphs that the result matches the given deviation quite well. If only the wc parameters are taken as condition parameters a large increase in compressor wc results. But as a 8.5% increase in flow capacity is clearly unrealistic, this result can be rejected. The actual deterioration is therefore found from the 1st_{exercise with which gave realistic results. After investigation of} cases 2 and 3, which are given in Appendix A, similar results are obtained.

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Figure 5 : GPA deterioration for case 1

Figure 6: GPA deterioration for case 1

The next cases that will be investigated are cases with deterioration in withtwo components. Cases 4 and 6 will be displayed here for this purpose. The graph for case 4 is shown in Figure 7 and that for case 6 in Figure 8. From these graphs it is clear that in case 6 the deterioration is returned very well using adaptive modeling. In case 4 however, there seems to be an ‘exchange’ between compressor and power turbine efficiency: some of the efficiency delta is ‘moved’ from compressor to gas generator turbine. This is because the effect on gas generator efficiency of delta’s of compressor and turbine efficiency is usually quite similar. The sum of the

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delta’s however remains the same. In GPS terms this is called the ‘smearing effect’. It can still be determined in this case that the deterioration is taking place in two components and therefore this result is adequate for gas path analysis purposes. Similar to the previous cases it is found that when the adaptive modeling looks for the solution in the the results are an improvement in the compressor, and these

results and can therefore easily be disregarded. The results for case 5 (Appendix A) are similar to those in cases 4 and 6 and are therefore not presented in this chapter.

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The final case of the first group (case 7) to be treated concerns the situation wherethere is deterioration in all three components. The results for this case are shown in Figure 9 and Figure 10. The result also matches the original input quite well when looking at the results for , and gives a nonsensical result when looking at , Figure 10. The results from cases 1 through 7 indicate that if there is only

deterioration occurring in within the engine, adaptive modeling is able to identify where the deterioration occurs.

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Deterioration in both and (Cases 8-14)

The next set of cases to be presented are the cases where deterioration in and wc occur simultaneously. The results for case 8 are shown in Figure 11 and Figure 12. Figure 11 indicates that the largest deviation is indeed in the compressor, although the value of the deterioration does not match. In Figure 12 however the deterioration found is opposite to the given value. The graphs together do point to the compressor as the deteriorated parameter, making this a positive result.

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The results for the next case, case 10, can be found in Figure 13 and Figure 14. As can be seen from these results, adaptive modeling is unable to indicate the correctly deteriorated component in this instance. Although the power turbine is the

deteriorated component in this case, the model seems to point at the compressor as the component in which deterioration occurs. This problem occurs in all the other cases from the second group (Appendix A). No matter where the deterioration occurs, the result always seems to indicate the compressor as the deteriorated component. Deterioration in the power turbine is never found.

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The final group of cases investigated are the ones where only deterioration occurs in the . The first lot of cases investigated are the cases where only a single

component shows deterioration. These are cases 15, 16 and 17.

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Firstly all of the graphs for case 15 are shown in Figure 15 and Figure 16. The graphs for this case seem to be ambiguous about which component shows the most deterioration. However the deterioration values for are significantly smaller than the ones for wc. The largest deterioration value is for the compressor when looking at the result for . This result therefore points at the compressor as the deteriorated

component.

For case 16, shown in Figure 17 and Figure 18, the opposite occurs. In this result the values when looking at are so large however that they can easily be discarded. Figure 18 then points to the gas generator turbine as the deteriorated component, again a good match.

Case 17 shows a similar result to case 15 and is therefore not presented here. When looking at cases 18, 19, and 20 similar trends are visible in the results as in the previous cases. Every time the graphs indicate a clear component as the

deteriorated component. The other condition parameters in every one of the cases show either significantly smaller values for the other condition parameters, similar to case 15. Or they show impossibly high values in on the other components, similar to case 16.

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Figure 18: GPA m˙ deterioration for case 16

Finally when looking at the results for case 21, shown in Figure 19 and Figure 20, it can be concluded that even for the case where deterioration occurs in all three components the simulated deterioration is found quite well using adaptive modeling methods.

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Figure 20: GPA m˙ deterioration for case 21

5.4. Limitations

In section 5.2 it is concluded that there are too few measurement parameters

available to investigate all the condition parameters simultaneously. This results in an approach where multiple calculations are needed from which ‘unlikely’ diagnostics information is to be eliminated. However, this introduces significant uncertainty, especially with a 3 conditions – 3 measurement parameter configuration. In some cases, a clear result may emerge but several cases can be imagined (especially when multiple components have deteriorated) where results will be false. This is a significant limitation in the applicability of differential gas path analysis for the BE1 engine. The BE2 engine has one more performance parameter (P3), but then still a 4x4 configuration remains with the same problem: multiple passes are required.

5.5. Conclusion

Component level diagnostics on the BE1 and BE2 engines is feasible using the GSP differential adaptive modeling GPA method. With component level condition

information that can be obtained during operation, maintenance costs can be reduced and reliability, availability and safety can be enhanced.

In practice, GPA analysis should be performed automatically on a routine basis at high power setting, which normally is at take-off. At end of take-off, normally sufficient

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stabilization towards steady-state is reached (avoiding transient effects) and a performance snapshot is taken. However, the exact procedure here has to be determined depending on the aircraft operating procedures.

In the current BE1 engine designs only 3 parameters are available for gas path analysis purposes. These are the exhaust gas temperature Tt5, fuel flow wf

(assuming fuel pressure can be translated to fuel flow) and the power turbine torque TRQpt [9]. For effective GPA however calculating all 6 condition parameters, 3 additional measurement parameters are needed. For the BE2 engine, also 3 parameters are available Tt5, TRQpt and P3 [9].

The GPA analysis can be done online, either in the aircraft or on the ground using a data link. Alternatively, the analysis can be done off-line post-flight on a ground work station. These aspects will be determined by the design of the overall condition monitoring and maintenance concept.

However, adding more sensors to the engines is not foreseen due to cost aspects. Therefore an alternative method is suggested to trend corrected or non-dimensional performance parameter groups relative to a baseline.

Should more sensors be added in the future on either the BE1 or BE2 engine, the differential GPA option can be implemented, for more detailed, i.e. component level condition information.

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6 Performance monitoring and trending concept

6.1. Introduction

In the sections above, it is concluded that differential GPA will not be practically feasible for the BE1 and BE2 engines due to the limited set of measured

performance data. In that case, an alternative approach may well be a better choice. Also, the higher costs of differential GPA cannot be easily justified for such small engine. Therefore, a performance monitoring and trending concept is developed and demonstrated, still offering engine condition indication, albeit not on the component level.

Trending is commonly performed on corrected parameters because it allows for reduced scatter in the performance of the tested engine. However, the number of these corrected parameters is limited and thus cannot be used for isolating

component performance deviations. This referred trending method relates GPA on an engine level. It’s limited because the type and location of deterioration, together with its severity cannot be identified. Parameter trending enables the maintenance provider to investigate parameter drifts over time and performance envelope.

6.2. Data

Availability

As already mentioned in section 5.1 the measurement data available (sensor location) along the gas path for the BE1 is represented in Figure 21. In this figure and the one thereafter, the compressor, gas generator turbine and power turbine are visualized schematically. The sensors that can provide useful measurement data for trend analysis are italicized.

For the BE1 engine, the sensors available are P1, T1, N1, N2, T5, Fuel pressure (we assume this can be used to accurately indicate fuel flow), and torque [9]. The available measurements will be used for baseline trending analysis. For the BE2 the sensor package is different [9] and is shown in Figure 22 and mentioned in Table 6. Thus the only additional referred group is . The method and analysis will be similar.

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For the BE2 engine the sensors are almost the same however the Wf is replaced by 3. Thus the could be used instead for referred baseline trending.

Figure 21 BE1 Available Sensors and useable measurements for trending (italic)

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6.3. Performance parameter trending (Parametric Investigation)

An alternative method of determining the turbine engine condition on the system level is parametric trend analysis of referred groups. Measurement data sets cannot be compared directly as each set is measured at different conditions. The ambient pressure, temperature and relative humidity vary over the flight envelope. To enable comparison of the measurement sets, the measurements have to be corrected to the same ambient conditions. The ambient conditions that need to be corrected are the ambient temperature and pressure. Relative humidity is the third ambient condition for which a correction can be applied. However, for the TT5 trending this is

unnecessary and furthermore is not done for the referred parameters below. The relative humidity has a negligible influence on the temperature. However, for other referred parameters it is suggested that the user checks the relative humidity’s influence to be sure. Thus in this report no correction for relative humidity is given. Table 2 outlines the parameter correction formulae that can be derived using

dimensional analysis (Buckingham Pi theorem), also described in [8] with the a and b exponents used in equation 5.

Table 2 Theoretical Referred Gas Turbine Parameters,

Parameter Referred Parameter

Pressure at station 0 1 , Temperature at station 1 0 , _Θ Rotor Speed 0.5 0 √Θ

Gas Mass Flow -0.5 1 _{∗ √Θ}

Fuel Flow 0.5 1 , √Θ ∗ Thrust 0 1 , Shaft Power 0.5 1 √Θ ∗ All corrected parameters take the following general mathematical form:

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The X stands for the referred parameter, n for the station number, Θ for the

temperature ratio 1/ and for the pressure ratio 1/ with the reference values usually taking on the ISA conditions 288.15 and 101325 . These are the theoretical referred parameters. We can then go to derived versions according to equation 5. These general theoretical exponents may not lead to a corrected shaft speed/power fully independent of ambient conditions. For further reduction of the dependency, the b and a must be changed/tweaked.

6.4. Corrected performance parameters and engine condition

Performance parameters measured in the gas path, fuel flow and power output, are sensitive to most gas path component conditions problems, such as deterioration caused by wear, fouling, erosion, corrosion of damage due to FOD or other mishaps. Some performance parameters are particularly suitable for this, including the EGT,

, Power output and SFC. GSP was used to investigate the above performance parameters on a system level. The ambient conditions are varied for an inlet temperature of 233.15K to 313.15K, an inlet pressure of 0.8 bars to 1.05 bars and corrected speed as a power setting from 106% to 80%. For performance monitoring T/O with the ambient conditions listed in Table 3 will be the point of interest. The rest of the flight envelope (for cruise power trending) will be analysed at a later date. . The BE1 operating conditions as given in [9] are

Table 3 BE1 operating conditions

Parameter Value

Pressure Altitude -300 to 9000m (-300 to 6000m for TS) Ambient temperature -40° to ISA +30

Flight Speed 0 to 0.6Ma

Table 4 BE1 start-up conditions

Parameter Value

Pressure Altitude -300 to 4000 m Ambient temperature -30° to 45°C Flight Speed 0 to 0.25Ma

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The following graphs show the variation of specifically chosen referred parameters vs corrected gas generator speed. These plots show data of the healthy engine. The derived trends and thus simulated data can be used as a baseline of theoretical performance of the BE1 engine. One can then compare the actual measured gas turbine performance with these referred trends.

The parameters chosen are generally those that have been measured. This allows the opportunity for the user to plot the measured values on the simulated curves whereby a deteriorated point can be seen as a deviation from the healthy data. Four baseline trends of a healthy engine are provided in Figure 23 through Figure 26. When the actual measured performance parameters are plotted on the baseline trends the user will be able to gather an estimate of the condition of the system. Thus deviations from the trend lines will indicate component/system deterioration. Important parameters missing are PT3 and TT4.5. However, TT5 exhaust gas temperature and also torque are measured and may well compensate for this. Fuel pressure may represent fuel flow but whether a consistent fuel flow-pressure relation exists has to be verified with more details on the control system. The control concept is assumed to be based on gas generator speed control, which represents power setting.

The chosen theoretical corrected parameters to be used for BE1 baseline trending are shown in Table 5.

Table 5 Theoretical corrected parameters for BE1 baseline trending

Corrected Parameter Equation

Ngg N1 √θ PW or TRQ _{√ ∗} or 5 T5 θ Wf √θ ∗

In reality the exponents a and b need to be changed if we want is a single base line that will always be consistent with any operating parameter. This is due to small secondary effects in the actual engine that cause deviations from the theoretical 0-D engine performance. If it were not possible to group all the curves we would need to first look up the condition and its corresponding trend which would be inconvenient.

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Due to the different sensors available on the BE1 and BE2 different groups may need to be analysed. The BE1 and BE2 have different sensor packages and thus require different baseline parameter trends.

Table 6 Theoretical corrected parameters for BE2 baseline trending

Corrected Parameter Equation

Ngg N1 √θ PW or TRQ _{√ ∗} or 5 T5 θ 3 ∗ 1

The following referred parameters are for the BE1 and BE2 engines. Power turbine exit temperature (TT5)

For the BE1:

The turbine inlet temperature is normally controlled by the engine control system. An increase in exhaust gas temperature T5 (or ‘EGT’) therefore indicates a deterioration of the performance of the engine. This is typically caused by damage to the shape of the blades and vanes due to oxidation, FOD, etc.

T5 indirectly indicates the gas generator turbine inlet temperature which is a primary limit beyond which the engine will rapidly deteriorate (corrosion or melting of stator vanes). TT45 (power turbine inlet temperature) would be a better indicator than TT5 but is not available. An increase in referred TT5 at the same power level indicates a degree of engine deterioration.

The margin between a healthy engine’s TT5 temperature and that of a deteriorated engine is determined to be

5 5

Instead of TT5, also TT5margin can be plotted to more directly indicate the difference between the engine condition and it’s limit, or, the point where the engine definitely must have maintenance.

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After aligning the TT5 and subsequently other referred parameters, for different temperature groups and pressure values the derived exponents for the TT5 referred parameter is given in Table 7.

Table 7 Adjusted/Derived referred TT5 parameter

TT5 temperature 1.03 0 5 _.5

Figure 23 is the trending of corrected TT5 vs corrected gas generator speed. The corrected TT5 takes the form of Equation (5) with a=1.03 and b=0. The corrected speed is with a=0.5 and b=0. Thus we have for this particular engine a single group corrected TT5 expression that will consistently always for a reference engine be on the same curve.

Figure 23 Corrected TT5 vs Corrected gas generator speed

Compressor pressure ratio For the BE2:

Table 8 Adjusted/Derived referred PR parameter

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PR 1 0 ∗3

1

The BE1 doesn’t have a sensor at PT3 but the BE2 does. Compressor deterioration will be shown by a lower pressure at the same Nc point on the curve.

Corrected Power output For the BE1:

Table 9 Adjusted/Derived referred PW parameter

PW 0.41 1.03 _.

∗ .

Figure 24 Corrected power vs Corrected gas generator speed

Specific fuel consumption

Table 10 Adjusted/Derived referred SFC parameter

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Figure 25 Corrected SFC vs Corrected gas generator speed

Corrected Fuel Flow

Table 11 Adjusted/Derived referred Wfc parameter

0.66 0.97 ∗ ∗ _.

∗ .

Figure 26 Corrected fuel flow vs Corrected gas generator speed

6.5. Remarks

It may be possible that in some cases thesimulated data are not very smooth. This comes from the fact that many different pressure lines and groups of temperature

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were swept for a range of power settings in GSP which were subsequently consolidated on a single curve.

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7 Measured Parameters and Baseline trends Deterioration

The BE1 does not have any real-time measured parameter values and thus the deterioration is simulated in GSP. These deterioration points are superimposed onto the baseline trends. These points (if deviating) can indicate deterioration on the system level. Simulated deterioration points are generated in GSP to try and ghost viable measured data that could be found in reality.

For different deterioration cases we can determine how far it is off the baseline. Deterioration effects are simulated for different components at design speed/power in order to see the deviation off the baseline healthy engine trend line.

If margins were defined different combinations of component deterioration could be simulated and its effect on the referred parameter analyzed.

For purposes of showing the concept, the TT5, Wf and PW will be evaluated with various cases of deterioration.

7.1. Design point deterioration cases

A number of deterioration situations will be defined for the High pressure compressor (HPC), Gas Generator Turbine (GGT) and the Power Turbine (PT), which could represent measured field data of an unhealthy engine. Each situation thus represents a steady state deteriorated point and will be subsequently plotted onto the referred parameter trend-lines of the healthy baseline engine already given in Figure 23, Figure 24 and Figure 26. These deterioration situations will be calculated for various flight settings (TO and Cruise). A very brief description of the defined deterioration situations follow, which will give an idea how sensitive the referred parameters are to deterioration.

Table 12, Table 13, Table 14 and Table 15 show various deterioration modifiers for the predominant turbomachinery components.

Compressor Deterioration In

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Table 12 compressor efficiency and corrected mass flow both decrease by 2%. The rest of the components deterioration modifiers are nil. Thus they are healthy and remain unchanged.

Table 12 Compressor component deterioration modifiers

Condition

Compressor

deterioration

▲

-2% -2% 0% 0% 0% 0%

Gas Generator Turbine Deterioration

Table 13 represents deterioration in the GGT by selection of deterioration modifiers representing a likely situation in the field. The other components are “healthy” so that we can see the influence of GGT deterioration on the selected referred parameter groups. A steady state point at TO is simulated and the figure symbol appears in Figure 27, Figure 28 and Figure 29.

Table 13 Gas generator component deterioration modifiers

Condition

Gas generator

deterioration

X

0% 0% -4% 2% 0% 0%

Turbine Deterioration

Table 14 Power Turbine component deterioration modifiers

Condition

Power Turbine

deterioration

■

0% 0% 0% 0% -4% 2%

Combination of component Deterioration

Table 15 Compressor component deterioration modifiers

Condition

Combination of

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7.2. Off-design

deterioration

cases

Deterioration effects can simulated on different points on the flight envelope. Take-Off is usually the best condition to take a performance snap shot for monitoring engine condition. This is because the power setting usually is fixed and ambient conditions not varying much (close to ISA sea level static) at Take-Off, minimizing the effects of varying power setting and flight conditions on the trend. In addition,

measurement uncertainty is usually minimal while stability of the operating point is best at the highest power setting. Therefore the Take-Off condition is chosen to demonstrate the trending method concept. The figures show one steady state simulated point per deterioration case at the design power. The deviation from the healthy baseline trend shows that somewhere in the engine there is a component with a fault. In reality it is hard to diagnose what exact component that could be (in this case we know because we simulated the data).

Take-Off operating condition (100%Nc_gg h=0, v=0)

Steady state deterioration points are simulated for TO (100%Nc_gg) at ISA conditions for three referred parameters TT5, PW and Wf.

TT5 referred parameter group

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The compressor deterioration situation (greed triangle) represents a -2% and -2% Wc. This corresponds to a 15K TT5 deviation.

PW referred parameter group

Figure 28 corrected PW vs corrected gas generator speed

Wf referred parameter group

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Looking at the three referred groups (TT5_c, Wf_c, PW_c) it is clear that the 5 is the most sensitive to the different deterioration situations. Due to the accuracy of the data at this flight condition it would also be prudent to analyse other conditions on the flight envelope and examine whether the same behavior is seen.

It should be kept in mind that referred parameter calibration and measurement uncertainty should be taken care of if test rig data is available. Deviations from ideality should be considered and referred parameters corrected if need be.

Measurement uncertainty such as noise and bias also leads to reduced validity and potential of the diagnostic results.

Note that in a real application, corrected gas generator rotor speed Nc_gg may well vary somewhat depending on control logic and due to ambient temperature

variations.

Other operating conditions

Although Take-Off is the best condition for performance monitoring of aero engines (see 7.2.1), other flight conditions can still be used for extracting additional engine condition information, using the similar graphs as shown in 7.2.1. However, this involves more complex data post processing schemes due to the large variation in flight conditions and power setting and therefore this is considered out of scope for the BE1 engine application.

7.3. Trending

Method

Limitations

The method may be used to trend the referred parameter groups where

measurement data is available. However, its diagnostic capability is limited due to a couple of reasons;

 The increase or decrease of the baseline healthy engine trended referred parameter is dependent of the type and location of the deterioration in the engine.

 Sensitive to measurement uncertainty

As can be seen from section 7.1 simulating specific deterioration effects in certain components influence the deviation from the baseline healthy engines trended

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referred parameter. It can be hard to tell if shifts from the line were caused by a small compressor efficiency deterioration or a large power turbine deterioration.

Measurement error could have a significant effect on any definable margins. The effects of deterioration are different for each location, which means that the severity of the deterioration and its location remains unknown.

Trending referred parameters should be used as a supplement to a maintenance program to give an indication of the engine and to warn for maximum referred parameter exceeding max values. For a more effective engine condition diagnostic procedure, a procedure that can identify performance changes on component level, such as the differential GPA adaptive modelling concept is advised. Thus this method is GPA limited to the system level.

7.4.

Implementation

The trending concept described in the section may be implemented on various levels. First, a simple database system could be developed for the condition monitoring ground station environment, for trending performance data, downloaded post-flight from the aircraft. The relations described above can be used for presenting the performance margin to the user in various forms and also provide a trending function per engine and/or for the entire fleet. In this case usually take-off performance snap shots are best used.

If an in-flight data link would be available such as with modern large airliners [10], also real-time monitoring and diagnostics could be performed on the ground. Then also cruise operating points can be used and monitored on a continuous basis if desired.

Ultimately, the margin calculation can be done on board, for example in the digital control system, to directly provide condition data to the pilot or to control or safety systems to take automatic measures. Obviously, these more advanced concepts may not be cost effective for small engines like the BE1 and BE2 turboprops.

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Note that the relations given in the report are developed using a provisional

performance model. This means that at implementation, the above exercise should be repeated in order to obtain the most accurate reference base line for the engines.

7.5.

Recommendations

More data from the turbine system are required which requires more sensors to be installed. This will improve GPA possibilities. Not only will this improve the model by allowing for deterioration in all 6 condition parameters simultaneously, if more than 6 measurement parameters are available it will be also possible to verify the results by performing adaptive modeling using different sets of measurements.

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8 Conclusions

Differential GPA and parametric trend analysis has been used to assess the

condition of the engine on a component base level and on an engine base level. GSP GPA adaptive modeling and its various approaches has been applied to the BE1 engine assuming sufficient measurement data is available.

Alternative methods such as the “parametric trend” approach have been developed in case there is not enough data available from the sensors. Various parameter trends have been obtained from GSP off-design analysis. Non-dimensional referred parameters have been used to remove the dependency on the operating conditions. Conclusions from trend analysis indicate that fixing one referred parameter fixes most other referred parameters. Thus a healthy engine on a system level can generate steady state data corresponding to various operating conditions. The generated trend lines can be used to draw useful conclusions on the overall performance of the engine. The fault cannot be quantified with this approach but gives a general idea regarding the condition of the machine based on deviations from the healthy trend lines.

5 is the referred parameter base line trend that captures the effect of deterioration in different components most efficiently. Small values of deterioration modifiers seem to propagate the most efficient visual effect in the 5 group.

These approaches are easily implemented in the BE2 engine due to the similar configuration.

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References

[1] W.P.J. Visser and M.J. Broomhead, ‘GSP, A Generic Object-Oriented Gas Turbine Simulation Environment’, ASME-2000-GT-0002

[2] W. P. J. Visser, O. Kogenhop and M. Oostveen, "A Generic Approach for Gas Turbine Adaptive Modeling," ASME Journal of Engineering for Gas Turbines and Power, vol. 128 GTP-04-1039, 2004.

[3] W. P. J. Visser, H. Pieters, M. Oostveen and E. v. Dorp, "Experience with GSP as a Gas Path Analysis Tool", ASME GT2006-90904, presented at the ASME TURBO EXPO 2006, Barcelona, Spain, 2006.

[4] Visser, W.; Head, A., ‘Engine simulation models for the cycles of BE1 and report on obtained performance data’, Esposa deliverable D.1.3.3.1 rev 2.0, Delft University TUD, August 201.

[5] Kogenhop, O. ‘Engine simulation models for the cycles of BE2 and report on obtained performance data’, Esposa deliverable D.1.3.3.2.

[6] GSP project file Eposa_D1331_BE1_rev2.0.mxl (provided in digital format). [7] GSP project file BE2_TPROP.mxl (provided in digital format).

[8] Kurzke, Joachim “Model based gas turbine parameter corrections” ASME-2003-38234

[9] Esposa deliverable ESP-D42.2 ‘Preliminary Design and Safety Analysis Report’, Version 1, 2013.

[10] Verbist M.L., Visser W.P.J., Pecnik R. Buijtenen J.P. van, ‘Experience with Gas Path Analysis for On-Wing Turbofan Condition Monitoring’, ASME paper GT2013-95739, presented at the ASME TURBO EXPO 2013, June 3-7, 2013, San Antonio, Texas, USA

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Appendix A Differential Gas Path Analysis Results

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Engine Health Monitoring Demonstrator based on GPA: A Gas Path Analysis GPA concept developed using the Gas turbine Simulation Program (GSP)

Engine Health Monitoring

Demonstrator based on GPA

Summary

Table of Contents

Acronyms

Indices

1 Introduction

2 The Gas turbine Simulation Program GSP

3 GSP BE1 turboprop engine model

4 GSP Adaptive Modeling Gas path analysis

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4.1. Reference

models

4.2. Selection of parameters

4.3. Measurement

uncertainty

5 GSP Differential Gas Path Analysis (GPA)

5.1. Preparing the engine model

5.2. Applying adaptive modeling to the BE1

5.3. GPA

Results

5.4. Limitations

5.5. Conclusion

6 Performance monitoring and trending concept

6.1. Introduction

6.2. Data

Availability

6.3. Performance parameter trending (Parametric Investigation)

6.4. Corrected performance parameters and engine condition

7 Measured Parameters and Baseline trends Deterioration

7.1. Design point deterioration cases

▲

X

■

7.2. Off-design

deterioration

cases

7.3. Trending

Method

Limitations

Implementation

Recommendations

8 Conclusions

References

Appendix A Differential Gas Path Analysis Results