With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a highquality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring.
In this paper, a novel compressor map tuning method is developed with the primary objective of improving the accuracy and fidelity of gas turbine engine models for performance prediction and diagnostics. A new compressor map fitting and modelling method is introduced to simultaneously determine the best elliptical curves to a set of compressor map data. The coefficients that determine the shape of the compressor map curves are analyzed and tuned through a multi-objective optimization scheme in order to simultaneously match multiple sets of engine performance measurements. The component map tuning method, that is developed in the object oriented Matlab Simulink environment, is implemented in a dynamic gas turbine engine model and tested in off-design steady state and transient as well as degraded operating conditions. The results provided demonstrate and illustrate the capabilities of our proposed method in refining existing engine performance models to different modes of the gas turbine operation. In addition, the excellent agreement between the injected and the predicted degradation of the engine model demonstrates the potential of the proposed methodology for gas turbine diagnostics. The proposed method can be integrated with the performance-based tools for improved condition monitoring and diagnostics of gas turbine power plants. Keywords: Component map, Model adaptation, Performance prediction, Gas turbine, Condition monitoring Highlights• A method for fitting rotated elliptical curves to compressor performance map data is presented.• The proposed fitting method is integrated into a dynamic model of a gas turbine.• The performance of the method is tested in steady state and transient conditions of gas turbine.• The proposed method is used to diagnose compressor fouling from transient data.
Gas turbines are faced with new challenges of increasing flexibility in their operation while reducing their life cycle costs, leading to new research priorities and challenges. One of these challenges involves the establishment of high fidelity, accurate, and computationally efficient engine performance simulation, diagnosis, and prognosis schemes, which will be able to handle and address the gas turbine's ever-growing flexible and dynamic operational characteristics. Predicting accurately the performance of gas turbines depends on detailed understanding of the engine components behavior that is captured by component performance maps. The limited availability of these maps due to their proprietary nature has been commonly managed by adapting default generic maps in order to match the targeted off-design or engine degraded measurements. Although these approaches might be suitable in small range of operating conditions, further investigation is required to assess the capabilities of such methods for use in gas turbine diagnosis under dynamic transient conditions. The diversification of energy portfolio and introduction of distributed generation in electrical energy production have created need for such studies. The reason is not only the fluctuation in energy demand but also more importantly the fact that renewable energy sources, which work with conventional fossil fuel based sources, supply the grid with varying power that depend, for example, on solar irradiation. In this paper, modeling methods for the compressor and turbine maps are presented for improving the accuracy and fidelity of the engine performance prediction and diagnosis. The proposed component map fitting methods simultaneously determine the best set of equations for matching the compressor and the turbine map data. The coefficients that determine the shape of the component map curves have been analyzed and tuned through a nonlinear multi-objective optimization scheme in order to meet the targeted set of engine measurements. The proposed component map modeling methods are developed in the object oriented matlab/simulink environment and integrated with a dynamic gas turbine engine model. The accuracy of the methods is evaluated for predicting multiple component degradations of an engine at transient operating conditions. The proposed adaptive diagnostics method has the capability to generalize current gas turbine performance prediction approaches and to improve performance-based diagnostic techniques.
The increase in energy demand has led to expansion of renewable energy sources and their integration into a more diverse energy mix. Consequently the operation of thermal power plants, which are spearheaded by the gas turbine technology, has been affected. Gas turbines are now required to operate more flexibly in grid supporting modes that include part load and transient operations. Therefore, condition based maintenance should encapsulate this recent shift in the gas turbine's role by taking into account dynamic operating conditions for diagnostic and prognostic purposes. In this paper, a novel scheme for performance-based prognostics of industrial gas turbines operating under dynamic conditions is proposed and developed. The concept of performance adaptation is introduced and implemented through a dynamic engine model that is developed in Matlab/Simulink environment for diagnosing and prognosing the health of gas turbine components. Our proposed scheme is tested under variable ambient conditions corresponding to dynamic operational modes of the gas turbine for estimating and predicting multiple component degradations. The diagnosis task developed is based on an adaptive method and is performed in a sliding window-based manner. A regression-based method is then implemented to locally represent the diagnostic information for subsequently forecasting the performance behavior of the engine. The accuracy of the proposed prognosis scheme is evaluated through the Probability Density Function (PDF) and the Remaining Useful Life (RUL) metrics. The results demonstrate a promising prospect of our proposed methodology for detecting and predicting accurately and efficiently the performance of gas turbine components as they degrade over time.
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