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One key aspect when developing a real-time in-flight risk-based health management system for jet engines is the development of accurate and robust fault classifiers. Regardless of the complex uncertainty propagation in the data fusion process, the selection of fault classifiers is the critical aspect of a health management system. The paper illustrates the application of a hybrid Stochastic-Fuzzy-Inference Model-Based System (StoFIS) to fault diagnostics and prognostics for both the engine performance. The random fluctuations of jet engine performance parameters during flight missions are modeled using multivariate stochastic models. The fault diagnostic and prognostic risks are computed using a stochastic model-based deviation (using a gas-path analysis model) approach. At any time the engine operation for the future is approached as a conditional reliability problem where the conditional data are represented by the past operational history monitored on-line by the engine health management (ERM) system. To capture the complex functional relationships between different engine performance parameters during flight fast an adaptive network-based fuzzy inference system is employed. This increases significantly the robustness of the ERM system during highly transient in-flight conditions. Both the monitored and fault data uncertainties are considered in a multidimensional parameter space, with two probabilistic-based safety margins employed for fault detection and diagnostic, as follows: (i) Anomaly Detection Margin (ADM) and (ii) Fault Detection Margin (FDM).
For the past several years, the Measurement and Computing Systems Laboratory has been working in close cooperation with the United States Air Force at Arnold Engineering Development Center (AEDC), Arnold AFB, to develop techniques for large scale instrumentation systems. In depth, online analysis of test data from turbine engine testing is critical to ensuring an accurate, timely evaluation and diagnosis of engine performance. Given the complexity of the analysis algorithms and the quantity of data, the computations overrun the capability of the fastest supercomputers. This paper describes the development of Computer Assisted Dynamic Data Monitoring and Acquisition System (CADDMAS). The CADDMAS is a 48 channel, 50 KHz full time analysis system, capable of flexible analysis of signals in the time and frequency domains. Data is presented on real time displays, showing, for example, spectrums, Campbell Diagrams, engine order tracking. The system (both hardware and software) is synthesized using a novel model based technique. The approach has been used to generate several systems used for online military and commercial turbine engine data analysis at Arnold AFB and for analysis of the SSME for NASA. On-line analysis has had a significant impact on turbine engine testing, reducing the time necessary to meet testing objectives and improving the quality of testing results. Substantial savings have been demonstrated by allowing immediate access to reduced data.
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.
A prototype USAF engine health management (EHM) system was developed and ground tested during this Phase II SBIR program. The EHM system is capable of real-time mechanical monitoring and diagnostics, aero-thermal performance monitoring and diagnostics, and "engine signature" based life accumulation. For the first time, state-of-the-art anomaly detection, monitoring, diagnosis and advanced life prediction analysis were integrated together in a single real-time engine health monitoring system. Additionally, the EHM system was developed to assist the 2-level maintenance concept and IHPTET initiatives.
A turbine engine diagnostic system utilizing a general purpose computer was developed and tested on a TF30-P-408 engine at sea level conditions. Forty-eight parameters, including vibration, oil and performance parameters, were monitored every 80 milliseconds. Forty-seven diagnostic messages were programmed and were displayed on a cathode ray tube. The system has not been fully debugged. Thirty-four diagnostic messages were demonstrated. (Author).
Whereas other books in this area stick to the theory, this book shows the reader how to apply the theory to real engines. It provides access to up-to-date perspectives in the use of a variety of modern advanced control techniques to gas turbine technology.