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In-service performance deterioration of gas turbine engines can be identified, at the engine module level, in terms of reductions in the component mass flow and the efficiency. Continued operation of a deteriorated gas turbine is (i) uneconomical and (ii) unsafe. Timely identification of the faults and ensuing maintenance could prevent both. Gas Path Analysis is a technique to establish the current performance level of the gas turbines and identify the faulty modules. Computer models can predict the off_design performance of gas turbines by aero-thermo-dynamically matching the engine components. This thesis describes the development of DETEM (DEeteriorated Turbine Engine Model), a generalised computer program, developed to model degraded gas turbine engines and analyse faults. The program has an integrated graphics module and creates windows on the VDU terminal,for displaying the program output and accepting the user input. This enables the user to compare the results of two different types of runs at the same time. The program incorporates sensor models that modify the output, with noise and in bias, based on the sensor characteristics, thus simulating a real engine. It is possible to simulate the engine performance at design point, off-design and under transient conditions. The runs could be for a "clean" and a deteriorated engine. Three techniques, iterative, fault coefficient matrix, and a statistical best-estimation technique, have been used to analyse the engine performance and identify the fault. Analysis of two and three shaft turbo-shaft engines and two spool turbo-fan and turbo-jet engines have been worked out in the thesis. Effects of reducing the number of measurements and measuring different engine parameters, on the accuracy of the fault identification, have been studied. The program is considered to have a potential for the generation of fault trees for rule-based expert system applied to gas turbine diagnostics. Because of the controlled output to the screen, a direct comparison of two different runs side by side, on the same screen, makes the program a good teaching aid for gas turbine diagnostics.

No description available.

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The gas turbine engine has a wide range of applications, these include industrial and aerospace applications on locomotive, ferry, compressor and power generation, and the most popular application will be for the air transportation. The application for air transportation including military and commercial aircraft is highly sensitive to safety concerns. The engine health monitoring system plays a major role for addressing this concern, a good engine monitoring system will not only to provide immediate and correct information to the engine user but also provide useful information for managing the maintenance activities. Without a reliable performance diagnosis module involved, there will be not possible to build a good health monitoring system. There are many methodologies had been proposed and studied during past three decades, and yet still struggling to search for some good techniques to handle instrumentation errors. In order to develop a reliable engine performance diagnosis technique, a fully understanding and proper handling of the instrumentation is essential. A engine performance fault pattern matching method has been proposed and developed in this study, two fault libraries contains a complete defined set of 51963 faults was created by using a newly serviced fighter engine component data. This pattern matching system had been verified by different approaches, such as compares with linear and nonlinear diagnosis results and compares with performance sensitivity analysis results by using LTF program engine data. The outcomes from the verications indicate an encouraging result for further exploring this method. In conclusion, this research has not only propose a feasible performance diagnosis techniques, but also developed and verified through different kind of approaches for this techniques. In addition to that, by proper manipulating the created fault library, a possible new tool for analyzing the application of instruments' implementation was discovered. The author believes there will be more to study by using this created fault pattern library. For instance, this fault pattern library can be treated as a very good initial training sets for neural networking to develop a neural diagnosis technique. This study has put a new milestone for further exploring gas turbine diagnosis technique by using fault pattern related methods.