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The Impact of Gas Turbine Component Leakage Fault on GPA Performance Diagnostics

The leakage analysis is a key factor in determining energy loss from a gas turbine. Once the components assembly fails, air leakage through the opening increases resulting in a performance loss. Therefore, the performance efficiency of the engine cannot be reliably determined, without good estimates and analysis of leakage faults. Consequently, the implementation of a leakage fault within a gas turbine engine model is necessary for any performance diagnostic technique that can expand its diagnostics capabilities for more accurate predictions. This paper explores the impact of gas turbine component leakage fault on GPA (Gas Path Analysis) Performance Diagnostics. The analysis is demonstrated with a test case where gas turbine performance simulation and diagnostics code TURBOMATCH is used to build a performance model of a model engine similar to Rolls-Royce Trent 500 turbofan engine, and carry out the diagnostic analysis with the presence of different component fault cases. Conclusively, to improve the reliability of the diagnostic results, a leakage fault analysis of the implemented faults is made. The diagnostic tool used to deal with the analysis of the gas turbine component implemented faults is a model-based method utilizing a non-linear GPA.


Introduction
The deterioration of the gas path components cannot be prevented in gas turbines which operate under normal and excessive environment conditions. The engine performance always degrades increasingly with time, impairing the airworthiness of the aircraft that could lead to catastrophic consequences. During operation, the components and ducts are also subject to an environment of thermal shocks, corrosion, mechanical vibration, and electromagnetic stresses, where the potential for damage and thus air leakage is high. Although, in the early years, gas turbine manufacturers have paid scant attention to the amount of leakage because the impact on performance measures was neglected, the designers' goal recently is to avoid any loss of the core flow to the environment. In this way, the work done by the engine on each amount of air saved can be used to increase thrust power. Therefore, air leakage diagnostics is a key factor in determining energy and accordingly performance losses of the engine. Gas path components efficiencies and engine operating characteristics cannot be reliably determined without good estimates of leakage fault. Specifically, for energy calculations, it is the duct or component leakage air flow to outside environment at operating conditions that is required. Simulating methods should either, precisely measure the size of leaks and thus the air flow along gas path with sufficient accuracy [1].
In an attempt to reduce the risk of such unwanted circumstances, commercial and military gas turbine users have engaged in some form of performance diagnostics [2]. Gas turbine performance diagnostic is a fairly mature methodology to accurately detect, isolate and assess the changes in engine module performance, engine system malfunctions and instrumentation problems from knowledge of measured parameters taken along the engine's gas path. Good estimates allow operators to make safe decisions, regarding the required maintenance actions. Different diagnostic approaches are adapted and developed, in order to restore the integrity and performance of the engine but one of the most popular is Gas Path Analysis (GPA) presented by Louis A. Urban [3]. GPA is a model-based mathematical technique that estimates individual modules and sensor performance shifts from any specified set of engine measurable parameters and component characteristics, through the aero-thermodynamic relationships which exist between them [4]. Non-linear GPA is a diagnostic tool that is preferred for use in this study because it is proved to offer a significant advantage over the severe limitations of linear GPA models since it addresses the non-linear nature of the engine thermodynamic behavior.
In this paper, the impact of the leakage fault is dealt with the diagnosis of implemented single and combined faults. The gas turbine diagnostic program used for this study is the GPA technique developed at Cranfield University [5]. The GPA technique has been applied to a gas turbine model engine, a civil high by-pass ratio turbofan engine, similar to Trent 500 manufactured by Rolls Royce plc [6][7][8].

Gas Path Analysis
Gas Path Analysis (GPA), pioneered by Urban is used to assess the condition of individual engine components, based on the aero-thermodynamic relationships that exist between the component and the direct measurements of gas path parameters [9]. The theory behind this relationship can be summarised by: The presence of a primary gas-path physical fault induces a change in the component characteristic that shows up as a deviation of the measurable parameters from the baseline conditions [10]. Therefore, the purpose of the GPA is to detect, isolate, and quantify the gas path components faults that have observable impacts on the measurable variables with the hope that will facilitate the subsequent isolation of the underlying physical fault. For a gas turbine engine, the mathematical relationship between dependent (engine component health parameters) and independent parameters (gas path measurements) is expressed analytically as [11]: The assumption of linearity becomes increasingly false, when deteriorations cause the engine to operate further away from the condition for which the matrix was calculated. Therefore, the non-linear GPA diagnostic technique is preferred at the present paper, instead of the linear technique, due to the consideration of the non-linear nature of the engine thermodynamic behavior [12,13]. The nonlinear GPA uses the Newton-Raphson iterative technique, where the linear GPA prediction process is applied iteratively until a converged solution is obtained [4].

Leakage Fault Isolation
The gas turbine operators during the diagnostic analysis barely have an indication of the degraded engine components. The occurrence of a fault is therefore unknown, and so it is necessary for a confident prediction to effectively isolate the degraded component. Consequently, the effective component isolation can lead to an accurate fault prediction of degradation, and for this purpose a GPA Index introduced in [14] and defined as: The parameter ε is a measure of the difference between the measured deviation (Δzο/zo) and predicted deviation (Δz/zo) of engine gas path measurements (fault signatures) in the ith state, expressed with Equation [3]: (3) Figure 1 illustrates the methodology that needs to be followed for the calculation of the GPA Index. Initially, the faulty engine components are defined and GPA predicts their degradation at certain ambient and operating conditions. The percentage deviations of the independent parameters are then sent to the model engine to estimate a predicted fault signature. Once both the real and predicted fault signatures are available, ε and thus GPA Index are calculated. If the predefined is the same to the implanted fault, ε tends to 0 and GPA Index gets closer to 1. Consequently, a GPA Index approaching the value of 1 indicates an accurate degradation prediction, otherwise the tendency to reach zero (0) is regarded as a poor degradation prediction result.  [14] According to [14], for an effective application of GPA Index to gas turbine component fault diagnosis, the maximum number of simultaneous degraded engine components and the component fault cases (CFC) that cover all the combinations of potential degraded components should be pre-defined. Following that, the diagnostic approach is employed to each of the CFC in order for the diagnostic model to cover all pre-defined engine faults (see Figure 2). The best solution or else the maximum value of GPA Index among all CFC indicates the most likely engine degradation case.

Fig. 2.
Diagnostic systems using GPA Index [14] For testing the diagnostic system, the engine faults implanted into the Trent 500 model engine include a component leakage, since it might impair the interpretation of the diagnostic results.

Analysis procedure of leakage fault impact
The proposed methodology in analyzing the impact of leakage fault on gas turbine GPA diagnostics is divided into the following main steps: -The simulated engine leakage faults are used as input to the GPA diagnostic system to isolate and detect them.

Model Engine
The engine model selected for the leakage impact analysis in this paper is similar to Rolls Royce Trent 500, a three spool, high-bypass turbofan engine rated in 249 kN net thrust at sea level. In Figure 3 there is a schematic representation of the model engine. The model engine was simulated using TURBOMATCH, a FORTRAN-based gas turbine simulator developed at Cranfield University [6,12,14,15]. Normally, it is appropriate to define DP of a gas turbine in cruise conditions because the aircraft spends most of the operational time at this situation. However, in the current paper the DP was chosen at the take-off condition because the available open access database for the performance parameters of this engine refer to the ground testing [16][17][18]. Therefore, the performance specifications of the engine at the DP are presented under the atmospheric conditions of SLS.

Instrumentation set selection
The success of any fault diagnosis technique depends critically on the sensor network, measuring the important observed parameters. An optimally designed sensor network for fault diagnosis should observe all the faults when they occur, and also distinguish between them to the maximum extent possible [19]. Therefore, the instrumentation set should be properly chosen for better detection of engine degradation because the quality of the diagnostic analysis relies on the quality of the measurements. Table 1 shows all the potential gas path measurements for the model engine and Table 2 all the health parameters of potential degraded engine components. For the measurement set to be effective in the GPA diagnostic analysis, the chosen measurements should be sensitive to engine degradation and independent from each other. To assist the selection of measurements, a sensitivity analysis is carried out, where the response of all the potential gas path measurements, due to unit deviation of each of the engine component health parameters, are obtained, by using performance simulation of the model engine.  Figure 4 illustrates the plotted sensitivity of all potential gas path measurements against all the model engine health parameters and the number of faults taken from Table 2. In an attempt to investigate the impact of leakage fault on performance diagnostics, engine component leakage fault degradation were implemented into the model engine using TURBOMATCH software and gas path measurements were simulated. The implemented degradation is assumed unknown to the engine users and simulated measurements are used to predict the seeded fault. As the linear GPA is less effective compared to its non-linear partner only the nonlinear GPA technique is used in diagnostic analysis.

Leakage Fault Analysis 4.3.1
Leakage Fault Diagnostics For the purpose of the leakage fault diagnostics, six (6) different Component Fault Cases (CFC), listed in Table 3 are examined. Every single and combined fault case that is implemented and then diagnosed, include at least one (1) type of a leakage degradation, with the code name L1 or Leak 1 and L2 or Leak 2. The location of each leakage type in the engine is the IP and HP Compressor respectively.  Table 4 and Figure 5 present the GPA predictions results for the first four single fault cases. Table 5 and Figure 6 present the predictions for all the combined fault degradations. From these Tables and Figures, it can be observed that the accuracy of the diagnosis is excellent because of the zero deviations of the detected from the implemented and expected degradation faults. Thereupon, the outcome of this investigation is that, the detection capability of non-linear GPA technique for any type of leakage fault degradation is remarkable.

Leakage Fault Isolation
For testing the diagnostic system, the engine faults implanted into the Trent 500 model engine include a component leakage, since it might impair the interpretation of the diagnostic results.

Single Degradation
In the case of a single degradation, the fault that is isolated is 50 percent air loss from IP Compressor and the mathematical technique used is non-linear GPA. For the purpose of the fault isolation, there is also an assumption that all gas path components, except of burners, are potential degraded components and the allowed maximum number of simultaneously degraded engine components is two. The exclusion of burner is made because its efficiency is high and the possibility to degrade rare. The GPA diagnostic system using GPA index described in Figure 1 and 2 used for the detection of the component fault cases is listed in Table 6. The results of the diagnostic analysis applied on every single and combined component set illustrated in the form of GPA Indices are shown in Figure 7. In some fault cases, no results and thus diagnostic predictions could be obtained but error messages appeared from the simulation code, reporting the end of the convergence iterations in zero or after very few loops. The reason for the unusual termination of the diagnostic process was that the percentage of health parameters had exceeded the allowed percentage TURBOMATCH can handle. Among all available solutions, Table 7 indicates the solutions with the highest accuracies, particularly with GPA Index over 0.9, and the faults that are most likely to be equal to the implemented one. It is clear from the Table 7 and Figure 8 that the common fault which seems to be appear through all the selected cases, is the leakage degradation, particularly the leakage that comes from the IP Compressor (Leak 1) with an average predicted value of 47.75 percent, is almost the percentage of the implemented value. There are also other degradations with smaller percentages such as 0.1 percent reduction in flow capacity and isentropic efficiency of IP Turbine (Turbine 2) in CFC 14 but they cannot be considered as accurate and true predictions. Consequently, it can be regarded that IP Compressor is the most likely degraded component with a detected fault similar to the implemented. A further consideration especially concerning the IP Compressor leakage predictions in Table 7, results that for all the four cases, the maximum deviation from the implanted value is around 2.25 percent, regarding of their average value. This can be explained because of the measurement correction errors and the influence of the unavoidable and unpredictable sensor bias and measurement noise.

Double Degradation
In the case of a double degradation, the fault that is isolated is 2 percent air loss from HP Compressor and 2 percent drop in flow capacity and isentropic efficiency in IP Turbine of the Trent 500 model engine. For the purpose of the current fault isolation, the same assumption holds as it was mentioned before in the single degradation, i.e. all gas path components are considered as potentially degraded except of the burners. The GPA diagnostic system described in Figure  1 and 2 will also be used for the search of all fault cases listed in Table 8. The results of the diagnostic analysis applied on every single and combined component set illustrated in the form of GPA Indices in Figure 9. In some fault cases, no results and thus diagnostic predictions could be obtained but error messages appeared from the simulation code, reporting the end of the convergence iterations in zero or very few loops. The reason for the unusual termination of the diagnostic process was the same as before for the single degradation, namely the percentage of health parameters had exceeded the allowed percentage TURBOMATCH can handle.  Table 9 indicates the solutions with the highest accuracies which are those with over 0.9 for the GPA Index, where the faults seem to be almost equal to the implemented values. It is obvious from Table 9 and Figure 10 that the most common faults that seem to appear through the selected cases, are the leakage degradation in HP Compressor (Leak 2) and the flow capacity with isentropic efficiency drop in the IP Turbine (Turbine 2). The percentages for all the degradations seem to be very close to the percentages values of the implemented faults and therefore it can be regarded that IP Compressor and IP Turbine are the most likely degraded components.

Conclusions
The objective of this paper is to diagnose and isolate component leakage faults on a gas turbine engine. The diagnostic program used for this paper was TURBOMATCH, a FORTRAN-based gas turbine simulator code developed at Cranfield University. For the implementation of the leakage degradation, non-linear GPA diagnostic technique was preferred instead of the linear GPA, due to its significant advantage over the severe limitations of linear GPA models. The aspects of the nonlinear GPA were applied to a simulated model of a high BPR turbofan aircraft engine. For the gas path diagnostics of the engine model, an appropriate instrumentation set that thermodynamically correlated with the desired faults was selected. The leakage degradation was initially implemented into the gas path components of the engine model and then diagnosed and isolated. The outcome of the results was very satisfactory, and proved that non-linear GPA is a very efficient diagnostic tool in building up the confidence in detecting, isolating and quantifying degraded components.