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This book presents new studies in the area of turbomachine mathematical modeling with a focus on models applied to developing engine control and diagnostic systems. The book contains one introductory and four main chapters. The introductory chapter describes the area of modeling of gas and wind turbines and shows the demand for further improvement of the models. The first three main chapters offer particular improvements in gas turbine modeling. First, a novel methodology for the modeling of engine starting is presented. Second, a thorough theoretical comparative analysis is performed for the models of engine internal gas capacities, and practical recommendations are given on model applications, in particular for engine control purposes. Third, multiple algorithms for calculating important unmeasured parameters for engine diagnostics are proposed and compared. It is proven that the best algorithms allow accurate prognosis of engine remaining lifetime.The field of wind turbine modeling is presented in the last main chapter. It introduces a general-purpose model that describes both aerodynamic and electric parts of a wind power plant. Such a detailed physics-based model will help with the development of more accurate control and diagnostic systems.In this way, this book includes four new studies in the area of gas and wind turbine modeling. These studies will be interesting and useful for specialists in turbine engine control and diagnostics.
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.
Diagnostic equipment and techniques to be included in a non-integrated system to detect and isolate the four most prevalent malfunctions in Navy aircraft gas turbine engines were selected. Each of twenty-five candidate elements were considered on the basis of technical feasibility, cost-effectiveness, and diagnostic need. Gas path analysis, with trending, and borescope inspection were selected as the most effective methods to diagnose the leading engine malfunctions (63%), foreign object damage and hot section distress. Other elements to be included in the proposed embryonic system are called oil analysis, time temperature recording indicator/low cycle fatigue counters, vibration testers, trim testers, test system vibration equipment, vibration signal analysis equipment, temperature sensing system tester, and Jetcal Analyzer. Oil anaylysis techniques being developed indicate a significant improvement compared to spectrometric analysis of diagnostic purposes. A summary of specific elements for utilization at each of three levels of maintenance and an engineering development plan with proposed implementation milestones are included. (Author).
Widely used for power generation, gas turbine engines are susceptible to faults due to the harsh working environment. Most engine problems are preceded by a sharp change in measurement deviations compared to a baseline engine, but the trend data of these deviations over time are contaminated with noise and non-Gaussian outliers. Gas Turbine Diagnostics: Signal Processing and Fault Isolation presents signal processing algorithms to improve fault diagnosis in gas turbine engines, particularly jet engines. The algorithms focus on removing noise and outliers while keeping the key signal features that may indicate a fault. The book brings together recent methods in data filtering, trend shift detection, and fault isolation, including several novel approaches proposed by the author. Each method is demonstrated through numerical simulations that can be easily performed by the reader. Coverage includes: Filters for gas turbines with slow data availability Hybrid filters for engines equipped with faster data monitoring systems Nonlinear myriad filters for cases where monitoring of transient data can lead to better fault detection Innovative nonlinear filters for data cleaning developed using optimization methods An edge detector based on gradient and Laplacian calculations A process of automating fault isolation using a bank of Kalman filters, fuzzy logic systems, neural networks, and genetic fuzzy systems when an engine model is available An example of vibration-based diagnostics for turbine blades to complement the performance-based methods Using simple examples, the book describes new research tools to more effectively isolate faults in gas turbine engines. These algorithms may also be useful for condition and health monitoring in other systems where sharp changes in measurement data indicate the onset of a fault.
Turbine engine diagnostics (TED) is a diagnostic expert system that aids the M1 Abrams' mechanic in finding and fixing problems in the AGT-1500 turbine engine. TED was designed to provide the apprentice mechanic the ability to diagnose and repair the turbine engine like an expert mechanic. This report discusses the reasoning method used in TED, called the procedural reasoning system (PRS), as well as various design considerations throughout the life of the project. The expert system was designed and built by the U.S. Army Research Laboratory (ARL) and the U.S. Army Ordnance Center and School (USAOC & S). TED has been fielded to both the Active Army and the National Guard.