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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.
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.
TED (turbine engine diagnostics) is a diagnostic expert system to help the MI Abrams' mechanic find and fix problems in the AGT15OO turbine engine. ThD was designed and built by the U.S. Army Research Laboratory and the U.S. Army Ordnance Center. Limited fielding was begun in July 1994 to selected National Guard units, with eventual fielding to 28 National Guard units. Active units of the U.S. Army will receive ThD in January 1996. Several foreign countries are expected to use TED for their M1 tank maintenance. TED was designed to provide the apprentice mechanic the ability to diagnose and repair the turbine engine like an expert mechanic. The U.S. Army Ordnance Center has estimated that TED will save more than $8 million annually by enhancing the Ml mechanic's diagnostic capabilities. (MM).
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).
Aimed at researchers, professors, practitioners, students and other computing professionals, this work focuses in genetic algorithms, reasoning under uncertainty, natural language processing, knowledge based technology, and neural networks.
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.