In gas turbine operation, engine performance and health status is very important information for engine operators. Such engine performance is normally represented by engine air flow rate, compressor pressure ratios, compressor isentropic efficiencies, turbine entry temperature, turbine isentropic efficiencies, etc. while the engine health status is represented by compressor and turbine efficiency indices and flow capacity indices, etc. However, these crucial performance and health information can not be directly measured and therefore are not easily available. In this research, a novel Adaptive Gas Path Analysis (Adaptive GPA) approach has been developed to estimate actual engine performance and gas path component health status by using gas path measurements, such as gas path pressures, temperatures, shaft rotational speeds, fuel flow rate, etc. Two steps are included in the Adaptive GPA approach, the first step is the estimation of degraded engine performance status by a novel application of a performance adaptation method and the second step is the estimation of engine health status at component level by using a new diagnostic method introduced in this paper based on the information obtained in the first step. The developed Adaptive GPA approach has been tested in four test cases where the performance and degradation of a model gas turbine engine similar to Rolls-Royce aero engine AVON-300 have been analyzed. The case studies have shown that the developed novel linear and non-linear Adaptive GPA approach can accurately and quickly estimate the degraded engine performance and predict the degradation of major engine gas path components with the existence of measurement noise. The test cases have also shown that the calculation time required by the approach is short enough for its potential online applications.

Accurate simulation and understanding of gas turbine performance is very useful for gas turbine users. Such a simulation and performance analysis must start from a design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be carried out. However, the initially simulated design-point performance of the engine using estimated engine component parameters may give a result that is different from the actual measured performance. This difference may be reduced with better estimation of these unknown component parameters. However, this can become a difficult task for performance engineers, let alone those without enough engine performance knowledge and experience, when the number of design-point component parameters and the number of measurable/target performance parameters become large. In this paper, a gas turbine design-point performance adaptation approach has been developed to best estimate the unknown design-point component parameters and match the available design-point engine measurable/target performance. In the approach, the initially unknown component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, air mass flow rate, cooling flows, bypass ratio, etc. The engine target (measurable) performance parameters may be thrust and specific fuel consumption for aero engines, shaft power and thermal efficiency for industrial engines, gas path pressures and temperatures, etc. To select, initially, the design point component parameters, a bar chart has been used to analyze the sensitivity of the engine target performance parameters to the design-point component parameters. The developed adaptation approach has been applied to a design-point performance matching problem of an industrial gas turbine engine GE LM2500+ operating in Manx Electricity Authority (MEA), UK. The application shows that the adaptation approach is very effective and fast to produce a set of design-point component parameters of a model engine that matches the actual engine performance very well. Theoretically, the developed techniques can be applied to other gas turbine engines.

Adaptive simulation technology enables the calibration of a performance simulation code to a given in-service gas turbine and provides correct prediction of its performance. This is a fundamental prerequisite for reliable gas-path diagnostics and performance health monitoring. In this paper, a new offdesign performance adaption algorithm is introduced. Cranfield University's consolidated engine performance simulation code PYTHIA is enhanced with the capability of offdesign performance adaptation to model available field data. The software minimizes, via a genetic algorithm, an objective function that measures the error between an initial engine model output and the real engine data by varying some characteristics' scaling factors. In this study, a multiple-point adaptation procedure was applied to a two-shaft aeroengine. This generated an optimized engine model that minimized its deviations from a set of test-bed data. The adapted model was then tested against different real data, resulting in an average error, over 8 measured parameters, of less than 0.35%. Nomenclaturea = weighting factor ETA = isentropic efficiency K = number of measurement N1 = relative low-pressure shaft speed, % N2 = relative high-pressure shaft speed, % n = number of offdesign points OF = objective function P = pressure, atm P = measurable performance-parameter vector PR = pressure ratio SF = scaling factor T = temperature, K u = ambient and operating-condition vector WAC = corrected mass flow rate, kg=s X = component-characteristics vector Subscripts amb = ambient DP = design point ETA = isentropic efficiency N = relative shaft speed OD = offdesign PR = pressure ratio WAC = corrected mass flow rate, flow capacity 0 = design point 6 = low-pressure compressor exit 8 = high-pressure compressor exit 11 = high-pressure turbine exit 15 = outlet fan turbine exit Superscript def = default

Transient and steady state data may contain the same essential fault information but some faults have been shown to be more easily detectable from transient data because the transient records provide significant diagnostic content especially as the fault effects are magnified under transient. Various traditional and conventional techniques such as fault trees, fault matrixes, gas path analysis and its variants have been applied to gas path fault diagnosis of gas turbines. Recently, artificial intelligence techniques such as artificial neural networks (ANN) as well as optimization techniques such as genetic algorithm (GA) are being explored for fault diagnosis activities. In this paper, a novel approach to gas path fault diagnosis is proposed. The method involves the use of ANN with engine transient data. A set of nested neural networks designed to estimate independent parameter (efficiencies and flow capacities) changes due to faults within single or multiple components of a turbofan engine are presented. The approach involves classification and approximation type networks. Measurements from the engine are first assessed by a trained network and if a fault is diagnosed, are then classified into two groups — those originating from sensor faults and those from component faults, by another trained network. Other trained networks continue the fault isolation process and finally the magnitude of the fault(s) is quantified. A computer simulation of the process shows that results from a batched process of these networks can be obtained in less than three seconds. Four of the gas path components — intermediate pressure compressor (IPC), high pressure compressor (HPC), high pressure turbine (HPT) and low pressure turbine (LPT) — and measurements from eight sensors are considered. Sensor noise and bias are also considered in this analysis. The comparison of fault signatures from a steady state and transient process show that diagnosis with transient data can improve the accuracy of gas turbine fault diagnosis.

Gas turbine gas path diagnostics is heavily dependent on performance simulation models accurate enough around a chosen diagnostic operating point, such as design operating point. With current technology, gas turbine engine performance can be predicted easily with thermodynamic models and computer codes together with basic engine design data and empirical component information. However the accuracy of the prediction is highly dependent on the quality of those engine design data and empirical component information such as component characteristic maps but such expensive information is normally exclusive property of engine manufacturers and only partially disclosed to engine users. Alternatively, estimated design data and assumed component information are used in the performance prediction. Yet, such assumed component information may not be the same as those of real engines and therefore poor off-design performance prediction may be produced. This paper presents an adaptive method to improve the accuracy of off-design performance prediction of engine models near engine design point or other points where detailed knowledge is available. A novel definition of off-design scaling factors for the modification of compressor maps is developed. A Genetic Algorithm is used to search the best set of scaling factors in order to adapt the predicted off-design engine performance to observed engine off-design performance. As the outcome of the procedure, new compressor maps are produced and more accurate prediction of off-design performance is provided. The proposed off-design performance adaptation procedure is applied to a model civil aero engine to test the effectiveness of the adaptive approach. The results show that the developed adaptive approach, if properly applied, has great potential to improve the accuracy of engine off-design performance prediction in the vicinity of engine design point although it does not guarantee the prediction accuracy in the whole range of off-design conditions. Therefore, such adaptive approach provides an alternative method in producing good engine performance models for gas turbine gas path diagnostic analysis.