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Загальна кількість знайдених документів : 2 Представлено документи з 1 до 2 |
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Loboda Igor Diagnostic analysis of gas turbine hot section temperature measurements [Електронний ресурс] / Igor Loboda, Yakov Feldshteyn, Fernanda Villarreal Claudia González // Авиационно-космическая техника и технология. - 2009. - № 6. - С. 66–79. - Режим доступу: http://nbuv.gov.ua/UJRN/aktit_2009_6_12 Temperatures measured in a hot section of gas turbines are very important for a gas path analysis. A suite of parallel thermocouples are usually installed in the same gas path station in order to compute a filtered and averaged temperature quantity for its further use in control and diagnostic systems. However, in spite of the preliminary treatment, the resulting quantity is not completely free from errors. To eliminate or reduce the errors, the present paper analyzes anomalies in the behaviour of each thermocouple of an industrial gas turbine engine. To that end, time graphs of both measured magnitudes themselves and their deviations from reference magnitudes are plotted. In order to draw sound conclusions, the analysis is conducted on a large volume of the data collected for three particular engines. | ||
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Loboda Igor Probability density estimation techniques for gas turbine diagnosis [Електронний ресурс] / Igor Loboda // Авиационно-космическая техника и технология. - 2013. - № 6. - С. 53–59. - Режим доступу: http://nbuv.gov.ua/UJRN/aktit_2013_6_11 In gas turbine engine condition monitoring systems, diagnostic algorithms based on measured gas path variables constitute an important component. Not only gas path faults are diagnosed by these algorithms, but also malfunctions of sensors and an engine control system can be identified with gas path measurements. Many gas path diagnostic algorithms use pattern classification techniques. In particular, a specific neural network, Multilayer Perceptron (MLP), is mostly applied. Unfortunately, the MLP cannot provide confidence estimation for its diagnostic decisions. However, there are techniques that classify patterns on the basis of probability. For example, Parzen Window and K-Nearest Neighbor methods compute probabilities of the considered classes estimating their probability densities. Thus, every diagnosis made is accompanied by its probability that is a very useful property for real gas turbine diagnosis. In the present paper, these two techniques are compared with the MLP in order to determine the technique that provides the best diagnostic accuracy on average for all possible gas turbine faults. The mentioned advantage of the Parzen Windows and K-Nearest Neighbors is also taken into account. | ||
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