Modeling of jet engine abnormal conditions and detection using the artificial immune system paradigm

Previous research at WVU has yielded promising results in the detection of aircraft sub-systems malfunctions using the artificial immune system (AIS) paradigm. However, one aircraft component that requires improvement is the aircraft propulsion system. In this research effort, MAPSS, a non-real time low-bypass turbofan engine model distributed by NASA, has been linearized and interfaced with the WVU F-15 model and the WVU 6 degrees-of-freedom flight simulator to provide a more complex engine model and create more options for engine failure modeling and engine failure detection. A variety of engine actuator and sensor failures were modeled and implemented into the simulation environment. A detection scheme based on the AIS approach was developed for specific classes of failures including throttle, burner fuel flow valve, variable nozzle area actuator, variable mixer area actuator, low-pressure spool speed sensor, low-pressure turbine exit static pressure sensor, and mixer pressure ratio sensor.;A 5-dimensional feature hyper-space is determined to build the self within the AIS paradigm for abnormal condition detection purposes. The WVU AIS interactive design environment based on evolutionary algorithms was used for data processing, detector generation, and limited optimization. Flight simulation data for system development and testing was acquired through experiments in the WVU 6 degrees-of-freedom flight simulator over extended areas of the flight envelope. The AIS-based detection scheme was tested using both nominal and engine failure conditions and its performance evaluated in terms of detection rates and false alarms. As compared to the previous failure detection results, significant improvement has been demonstrated as well as excellent potential for detection of the newly modeled engine failures

A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults

A challenging problem in aircraft engine health management (EHM) system development is to detect and isolate faults in system components (i.e., compressor, turbine), actuators, and sensors. Existing nonlinear EHM methods often deal with component faults, actuator faults, and sensor faults separately, which may potentially lead to incorrect diagnostic decisions and unnecessary maintenance. Therefore, it would be ideal to address sensor faults, actuator faults, and component faults under one unified framework. This paper presents a systematic and unified nonlinear adaptive framework for detecting and isolating sensor faults, actuator faults, and component faults for aircraft engines. The fault detection and isolation (FDI) architecture consists of a parallel bank of nonlinear adaptive estimators. Adaptive thresholds are appropriately designed such that, in the presence of a particular fault, all components of the residual generated by the adaptive estimator corresponding to the actual fault type remain below their thresholds. If the faults are sufficiently different, then at least one component of the residual generated by each remaining adaptive estimator should exceed its threshold. Therefore, based on the specific response of the residuals, sensor faults, actuator faults, and component faults can be isolated. The effectiveness of the approach was evaluated using the NASA C-MAPSS turbofan engine model, and simulation results are presented

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

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 high-quality 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

Aeronautical engineering: A continuing bibliography with indexes (supplement 257)

This bibliography lists 560 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1990. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Design Considerations of Low Bypass Ratio Mixed Flow Turbofan Engines with Large Power Extraction

The possibility of extracting large amounts of electrical power from turbofan engines is becoming increasingly desirable from an aircraft perspective. The power consumption of a future fighter aircraft is expected to be much higher than today\u27s fighter aircraft. Previous work in this area has concentrated on the study of power extraction for high bypass ratio engines. This motivates a thorough investigation of the potential and limitations with regards to performance of a low bypass ratio mixed flow turbofan engine. A low bypass ratio mixed flow turbofan engine was modeled, and key parts of a fighter mission were simulated. The investigation shows how power extraction from the high-pressure turbine affects performance of a military engine in different parts of a mission within the flight envelope. An important conclusion from the analysis is that large amounts of power can be extracted from the turbofan engine at high power settings without causing too much penalty on thrust and specific fuel consumption, if specific operating conditions are fulfilled. If the engine is operating (i) at, or near its maximum overall pressure ratio but (ii) further away from its maximum turbine inlet temperature limit, the detrimental effect of power extraction on engine thrust and thrust specific fuel consumption will be limited. On the other hand, if the engine is already operating at its maximum turbine inlet temperature, power extraction from the high-pressure shaft will result in a considerable thrust reduction. The results presented will support the analysis and interpretation of fighter mission optimization and cycle design for future fighter engines aimed for large power extraction. The results are also important with regards to aircraft design, or more specifically, in deciding on the best energy source for power consumers of the aircraft

Gas turbine transient performance simulation, control and optimisation

A gas turbine engine is a complex and non-linear system. Its dynamic response changes at different operating points. The exogenous inputs: atmospheric conditions and Mach number, also add disturbances and uncertainty to the dynamic. To satisfy the transient time response as well as safety requirements for its entire operating range is a challenge for control system design in the gas turbine industry. Although the recent design of engine control units includes some advanced control techniques to increase its control robustness and adaptability to the changing environment, the classic scheduling technique still plays the decisive role in determining the control values due to its better reliability under normal circumstances. Producing the schedules requires iterative experiments or simulations in all possible circumstances for obtaining the optimal engine performance. The techniques, such as scheduling method or linear control methods, are still lack of development for control of transient performance on most commercial simulation tools. Repetitive simulations are required to adjust the control values in order to obtain the optimal transient performance. In this project, a generalised model predictive controller was developed to achieve an online transient performance optimisation for the entire operating range. The optimal transient performance is produced by the controller according to the predictions of engine dynamics with consideration of constraints. The validation was conducted by the application of the control system on the simulated engines. The engines are modelled to component-level by the inter-component volume method. The results show that the model predictive controller introduced in this project is capable of providing the optimal transient time response as well as operating the engine within the safety margins under constant or varying environmental conditions. In addition, the dynamic performance can be improved by introducing additional constraints to engine parameters for the specification of smooth power transition as well as fuel economy

Turbofan Engine Behaviour Forecasting using Flight Data and Machine Learning Methods

The modern gas turbine engine widely used for aircraft propulsion is a complex integrated system which undergoes deterioration during operation due to the degradation of its gas path components. This dissertation outlines the importance of Engine Condition Monitoring (ECM) for a more efficient maintenance planning. Different ML approaches are compared with the application of predicting engine behaviour aiming at finding the optimal time for engine removal. The selected models were OLS, ARIMA, NeuralProphet, and Cond-LSTM. Long operating and maintenance history of two mature CF6-80C2 turbofan engines were used for the analysis, which allowed for the identification of the impact of different factors on engine performance. These factors were also considered when training the ML models, which resulted in models capable of performing prediction under specified operation and flight conditions. The Machine Learning (ML) models provided forecasting of the Exhaust Gas Temperature (EGT) parameter at take-off phase. Cond-LSTM is shown to be a reliable tool for forecasting engine EGT with a Mean Absolute Error (MAE) of 7.64?, allowing for gradual performance deterioration under specific operation type. In addition, forecasting engine performance parameters has shown to be useful for identifying the optimal time for performing important maintenance action, such as engine gas path cleaning. This thesis has shown that engine removal forecast can be more precise by using sophisticated trend monitoring and advanced ML methods.O moderno motor de turbina a gás amplamente utilizado para propulsão de aeronaves é um sistema integrado complexo que sofre deterioração durante a operação devido à degradação de seus componentes do percurso do gás. Esta dissertação destaca a importância da monitorização da condição do motor para um planejamento de manutenção mais eficiente. Diferentes abordagens de Machine Learning (ML) são comparadas visando a aplicação de previsão do comportamento do motor com o objetivo de encontrar o momento ideal para a remoção do motor. Os modelos selecionados foram OLS, ARIMA, NeuralProphet e Cond-LSTM. O longo histórico de operação e manutenção de dois motores turbofan CF6-80C2 maduros foi usado para a análise, o que permitiu a identificação do impacto de diferentes fatores no desempenho do motor. Esses fatores também foram considerados no treinamento dos modelos de ML, o que resultou em modelos capazes de realizar a previsão em operação e condições de voo especificadas. Os modelos ML forneceram previsão do parâmetro Exhaust Gas Temperature (EGT) na fase de decolagem. O Cond-LSTM demonstrou ser uma ferramenta confiável para previsão do EGT do motor com um erro absoluto médio de 7,64 ?, permitindo a deterioração gradual do desempenho sob um tipo específico de operação. Além disso, a previsão dos parâmetros de desempenho do motor tem se mostrado útil para identificar o momento ideal para realizar ações de manutenção importantes, como a limpeza do percurso do gás do motor. Esta tese mostrou que a previsão de remoção do motor pode ser mais precisa usando um monitoramento sofisticado de tendências e métodos avançados de ML

Aeronautical engineering: A continuing bibliography with indexes (supplement 295)

This bibliography lists 581 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System in Sep. 1993. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Gas turbine aero-engines real time on-board modelling: A review, research challenges, and exploring the future

On-board real time modelling for gas turbine aero-engines has been extensively used for engine performance improvement and reliability. This has been achieved by the utilization of on-board model for the engine's control and health management. This paper offers a historical review of on-board modelling applied on gas turbine engines and it also establishes its limitations, and consequently the challenges, which should be addressed to apply the on-board real time model to new and the next generation gas turbine aero-engines. For both applications, i.e. engine control and health management, claims and limitations are analysed via numerical simulation and publicly available data. Regarding the former, the methods for modelling clean and degraded engines are comprehensively covered. For the latter, the techniques for the component performance tracking and sensor/actuator diagnosis are critically reviewed. As an outcome of this systematic examination, two remaining research challenges have been identified: firstly, the requirement of a high-fidelity on-board modelling over the engine life cycle, especially for safety-critical control parameters during rapid transients; secondly, the dependability and reliability of on-board model, which is critical for the engine protection in case of on-board model failure. Multiple model-based on-board modelling and runtime assurance are proposed as potential solutions for the identified challenges and their potential and effectiveness are discussed in detail