Gas turbine engine health monitoring by fault pattern matching method

Abstract

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.