Aircraft Engine Diagnostics
Aircraft Engine Diagnostics

Author: National Aeronautics and Space Administration

Publisher: Createspace Independent Publishing Platform

Published: 2014-01-19

Total Pages: 388

ISBN-13: 9781495250705

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During the past four decades, the Lewis Research Center has been providing advances in aeronautical propulsion from the research activities of its staff and its university and industrial grantees and contractors. These advances have helped create the preeminence in aeronautics that has contributed to our national defense, has provided swift and reliable transportation for our people and their goods, and has greatly aided our position in international trade. In recent years substantial resources have also been directed at improving our nation's utilization of energy. NASA is well aware that the aviation industry is an important segment of our national economy. In 1979 aircraft sales led all U.S. manufacturing industries with a trade surplus of over $10 billion - without which the country would have experienced a one-third greater trade deficit. This favorable balance attributable to the aircraft industry is largely a result of being able to provide a superior product and to continue to upgrade the product. Efforts at improving the performance retention of today's and future engines which will power commercial and military aircraft represent a positive step toward this end. To provide to representatives from government, industry, and universities the latest findings directly related to improved aircraft engine performance retention, a two-day conference was held in May 1981. This publication contains the papers presented at that conference.

Diagnostics and Prognostics of Aerospace Engines
Diagnostics and Prognostics of Aerospace Engines

Author: Ravi Rajamani

Publisher: SAE International

Published: 2018-11-28

Total Pages: 196

ISBN-13: 0768095395

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The propulsion system is arguably the most critical part of the aircraft; it certainly is the single most expensive component of the vehicle. Ensuring that engines operate reliably without major maintenance issues is an important goal for all operators, military or commercial. Engine health management (EHM) is a critical piece of this puzzle and has been a part of the engine maintenance for more than five decades. In fact, systematic condition monitoring was introduced for engines before it was applied to other systems on the aircraft. Diagnostics and Prognostics of Aerospace Engines is a collection of technical papers from the archives of SAE International, which introduces the reader to a brief history of EHM, presents some examples of EHM functions, and outlines important future trends. The goal of engine health maintenance is ultimately to reduce the cost of operations by catching problems before they become major issues, by helping reduce repair times through diagnostics, and by facilitating logistic optimization through prognostic estimates. Diagnostics and Prognostics of Aerospace Engines shows that the essence of these goals has not changed over time.

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics
A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

Author: Takahisa Kobayashi

Publisher:

Published: 2001

Total Pages: 18

ISBN-13:

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In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics
Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

Author:

Publisher:

Published: 2003

Total Pages: 18

ISBN-13:

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In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated. (7 tables, 4 figures, 17 refs.).

Hybrid Kalman Filter
Hybrid Kalman Filter

Author: National Aeronautics and Space Administration (NASA)

Publisher: Createspace Independent Publishing Platform

Published: 2018-06-24

Total Pages: 26

ISBN-13: 9781721832293

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In this paper, a uniquely structured Kalman filter is developed for its application to in-flight diagnostics of aircraft gas turbine engines. The Kalman filter is a hybrid of a nonlinear on-board engine model (OBEM) and piecewise linear models. The utilization of the nonlinear OBEM allows the reference health baseline of the in-flight diagnostic system to be updated to the degraded health condition of the engines through a relatively simple process. Through this health baseline update, the effectiveness of the in-flight diagnostic algorithm can be maintained as the health of the engine degrades over time. Another significant aspect of the hybrid Kalman filter methodology is its capability to take advantage of conventional linear and nonlinear Kalman filter approaches. Based on the hybrid Kalman filter, an in-flight fault detection system is developed, and its diagnostic capability is evaluated in a simulation environment. Through the evaluation, the suitability of the hybrid Kalman filter technique for aircraft engine in-flight diagnostics is demonstrated. Kobayashi, Takahisa and Simon, Donald L. Glenn Research Center NASA/TM-2006-214491, E-15783, ARL-TR-4001

Aircraft Engine Sensor/Actuator/Component Fault Diagnosis Using a Bank of Kalman Filters
Aircraft Engine Sensor/Actuator/Component Fault Diagnosis Using a Bank of Kalman Filters

Author: National Aeronautics and Space Administration (NASA)

Publisher: Createspace Independent Publishing Platform

Published: 2018-05-29

Total Pages: 40

ISBN-13: 9781720375425

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In this report, a fault detection and isolation (FDI) system which utilizes a bank of Kalman filters is developed for aircraft engine sensor and actuator FDI in conjunction with the detection of component faults. This FDI approach uses multiple Kalman filters, each of which is designed based on a specific hypothesis for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, from which a specific fault is isolated. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The performance of the FDI system is evaluated against a nonlinear engine simulation for various engine faults at cruise operating conditions. In order to mimic the real engine environment, the nonlinear simulation is executed not only at the nominal, or healthy, condition but also at aged conditions. When the FDI system designed at the healthy condition is applied to an aged engine, the effectiveness of the FDI system is impacted by the mismatch in the engine health condition. Depending on its severity, this mismatch can cause the FDI system to generate incorrect diagnostic results, such as false alarms and missed detections. To partially recover the nominal performance, two approaches, which incorporate information regarding the engine s aging condition in the FDI system, will be discussed and evaluated. The results indicate that the proposed FDI system is promising for reliable diagnostics of aircraft engines.Kobayashi, Takahisa and Simon, Donald L. (Technical Monitor)Glenn Research CenterAIRCRAFT ENGINES; FAULT DETECTION; KALMAN FILTERS; SYSTEMS HEALTH MONITORING; SENSORS; ACTUATORS; COMPONENT RELIABILITY; NONLINEARITY; COMPUTERIZED SIMULATION; DEGRADATION; ENGINE PARTS; ERROR ANALYSIS; PROPULSION SYSTEM PERFORMANCE; STEADY STATE

Enhanced Self Tuning On-Board Real-Time Model (Estorm) for Aircraft Engine Performance Health Tracking
Enhanced Self Tuning On-Board Real-Time Model (Estorm) for Aircraft Engine Performance Health Tracking

Author: National Aeronautics and Space Administration (NASA)

Publisher: Createspace Independent Publishing Platform

Published: 2018-05-22

Total Pages: 56

ISBN-13: 9781719488730

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A key technological concept for producing reliable engine diagnostics and prognostics exploits the benefits of fusing sensor data, information, and/or processing algorithms. This report describes the development of a hybrid engine model for a propulsion gas turbine engine, which is the result of fusing two diverse modeling methodologies: a physics-based model approach and an empirical model approach. The report describes the process and methods involved in deriving and implementing a hybrid model configuration for a commercial turbofan engine. Among the intended uses for such a model is to enable real-time, on-board tracking of engine module performance changes and engine parameter synthesis for fault detection and accommodation. Volponi, Al and Simon, Donald L. (Technical Monitor) Glenn Research Center TURBOFAN ENGINES; AIRCRAFT ENGINES; FAULT DETECTION; GAS TURBINE ENGINES; PERFORMANCE PREDICTION; REAL TIME OPERATION; DIAGNOSIS; DATA PROCESSING; PROPULSION