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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.
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.
The initial focus of TEDANN is on AGT-1500 fuel flow dynamics: that is, fuel flow faults detectable in the signals from the Electronic Control Unit's (ECU) diagnostic connector. These voltage signals represent the status of the Electro-Mechanical Fuel System (EMFS) in response to ECU commands. The EMFS is a fuel metering device that delivers fuel to the turbine engine under the management of the ECU. The ECU is an analog computer whose fuel flow algorithm is dependent upon throttle position, ambient air and turbine inlet temperatures, and compressor and turbine speeds. Each of these variables has a representative voltage signal available at the ECU's J1 diagnostic connector, which is accessed via the Automatic Breakout Box (ABOB). The ABOB is a firmware program capable of converting 128 separate analog data signals into digital format. The ECU's J1 diagnostic connector provides 32 analog signals to the ABOB. The ABOB contains a 128 to 1 multiplexer and an analog-to-digital converter, CP both operated by an 8-bit embedded controller. The Army Research Laboratory (ARL) developed and published the hardware specifications as well as the micro-code for the ABOB Intel EPROM processor and the internal code for the multiplexer driver subroutine. Once the ECU analog readings are converted into a digital format, the data stream will be input directly into TEDANN via the serial RS-232 port of the Contact Test Set (CTS) computer. The CTS computer is an IBM compatible personal computer designed and constructed for tactical use on the battlefield. The CTS has a 50MHz 32-bit Intel 80486DX processor. It has a 200MB hard drive and 8MB RAM. The CTS also has serial, parallel and SCSI interface ports. The CTS will also host a frame-based expert system for diagnosing turbine engine faults (referred to as TED; not shown in Figure 1).
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.
This Proceedings contains the papers presented at the 14th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2001), held in Manchester, UK, on 4-6 September 2001. COMADEM 2001 builds on the excellent reputation of previous conferences in this series, and is essential for anyone working in the field of condition monitoring and maintenance management.The scope of the conference is truly interdisciplinary. The Proceedings contains papers from six continents, written by experts in industry and academia the world over, bringing together the latest thoughts on topics including: Condition-based maintenance Reliability centred maintenance Asset management Industrial case studies Fault detection and diagnosis Prognostics Non-destructive evaluation Integrated diagnostics Vibration Oil and debris analysis Tribology Thermal techniques Risk assessment Structural health monitoring Sensor technology Advanced signal processing Neural networks Multivariate statistics Data compression and fusion This Proceedings also contains a wealth of industrial case studies, and the latest developments in education, training and certification. For more information on COMADEM's aims and scope, please visit http://www.comadem.com
Turbine engine diagnostics have been vastly improved with the use of Artificial Intelligence (AI) techniques such as expert systems artificial neural networks and fuzzy logic. A typical system that is using artificial intelligence to improve its diagnostic capabilities is the Army's Turbine Engine Diagnostic (TED) program for the Ml Abram's AGT-1500 turbine engine. TED is a diagnostic expert system that assists the Ml Abrams mechanic. The system provides assistance during engine inspection and troubleshooting. It provides detailed information about the most frequently used maintenance procedures. It has an automated parts ordering system. Finally it has a diagnostics tool capable of monitoring the engine's electronic signals.
Abstract: Modeling of turbine engines has been an important topic for a long time, because of its importance in simulation and controller design. This research work has concentrated on turbine engine model development, and control of turbine engines using MATLAB / SIMULINK. The model simulates the dynamics of the various turbine engine components. Mathematical equations derived from first principles are used for modeling the system. Algebraic equations model the thermodynamic processes, while differential equations are used to model the dynamic nature of the turbine engine. The dynamics of the turbine engine can be studied using this SIMULINK model. Engine health monitoring systems (including engine diagnostics and prognostics) can help reduce the total labor needed to maintain engines and allow planning of maintenance schedules to accomplish preventive maintenance more effectively. various failure detection techniques available in the literature are reviewed in order to perform Turbine Engine Diagnostics. The benefits of using a Bank of Kalman filters for sensor fault diagnostics were understood and the technique is applied for diagnostics of the Simulink model of the turbine engine developed. A literature review of turbine engine prognostic techniques reveals the potential of model-based prognostic methods to accurately predict the remaining useful life of various turbine engine components.