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Diagnostic equipment and techniques to be included in a non-integrated system to detect and isolate the four most prevalent malfunctions in Navy aircraft gas turbine engines were selected. Each of twenty-five candidate elements were considered on the basis of technical feasibility, cost-effectiveness, and diagnostic need. Gas path analysis, with trending, and borescope inspection were selected as the most effective methods to diagnose the leading engine malfunctions (63%), foreign object damage and hot section distress. Other elements to be included in the proposed embryonic system are called oil analysis, time temperature recording indicator/low cycle fatigue counters, vibration testers, trim testers, test system vibration equipment, vibration signal analysis equipment, temperature sensing system tester, and Jetcal Analyzer. Oil anaylysis techniques being developed indicate a significant improvement compared to spectrometric analysis of diagnostic purposes. A summary of specific elements for utilization at each of three levels of maintenance and an engineering development plan with proposed implementation milestones are included. (Author).
A selection of annotated references to unclassified reports and journal articles that were introduced into the NASA scientific and technical information system and announced in Scientific and technical aerospace reports (STAR) and International aerospace abstracts (IAA)
The gas turbine which has found numerous applications in Air, Land and Seaapplications, as a propulsion system, electricity generator and prime mover, issubject to deterioration of its individual components. In the past, variousmethodologies have been developed to quantify this deterioration with varyingdegrees of success. No single method addresses all issues pertaining to gasturbine diagnostics and thus, room for improvement exists. The first part of thisresearch investigates the feasibility of non-linear W eighted Least Squares as agas turbine component deterioration quantification tool. Two new weightingschemes have been developed to address measurement noise. Four caseshave been run to demonstrate the non-linear weighted least squares method, inconjunction with the new weighting schemes. Results demonstrate that thenon-linear weighted least squares method effectively addresses measurementnoise and quantifies gas path component faults with improved accuracy over itslinear counterpart and over methods that do not address measurement noise. Since Gas turbine diagnostics is based on analysis of engine performance atgiven ambient and power setting conditions; accurate and reliable engineperformance modelling and simulation models are essential for meaningful gasturbine diagnostics. The second part of this research therefore sought todevelop a multi-fuel and multi-caloric simulation method with the view ofimproving simulation accuracy. The method developed is based on non-linearinterpolation of fuel tables. Fuel tables for Jet-A, UK Natural gas, Kerosene andDiesel were produced. Six case studies were carried out and the resultsdemonstrate that the method has significantly improved accuracy over linearinterpolation based methods and methods that assume thermal perfection.
The performance diagnostics of any engine model is accomplished by estimating a set of internal engine health parameters from available sensor measurements. These sensors which comprises of a variety of gas path measurements e.g. pressures, temperatures, fuel flow and spool speeds provide information regarding the health of the engine. No physical measurement, however, elaborate or precise, or how often repeated, can ever completely eliminate the universal presence of measurement uncertainties. Instrument measurements are often distorted by noise and bias, thereby masking the true condition of the engine leads to incorrect estimation results. Measurement uncertainties encourage the inaccurate fault diagnosis, and in order to improve the reliability of diagnostic results, it is important to statistically analyse the data scattering caused by sensor noise. Leakage analysis is a key factor in determining energy losses from a gas turbine. Once the components assembly fails, air leakage through the opening increases resulting in a performance loss. Therefore, the performance efficiency of the engine cannot be reliably determined, without good estimates and analysis of leakage faults. Specifically, for energy calculations it is the air flow leaking around the components at operating conditions that is required. Consequently the implementation of a leakage fault within a gas turbine engine model is necessary for any diagnostic technique that can expand its diagnostics capabilities for more accurate predictions. The simulating methods should either, precisely measure the size of leaks or measure the air flow along gas path with sufficient accuracy. In this research, the diagnostic tool that used to deals with the statistical analysis of measurement noise and leakage fault diagnostics is a model-based method utilizing non-linear GPA. For the purpose of diagnostic, the simulation code used in this study is TURBOMATCH and the engine model Trent 500. TURBOMATCH is the name of a.