Semiparametric statistical analysis of the Blade Tip Timing data for detection of turbine rotor speed instabilities.

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388998%3A_____%2F17%3A00482024" target="_blank" >RIV/61388998:_____/17:00482024 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/17:00482024

Výsledek na webu

<a href="http://www.enbis.org/activities/events/current/534_ENBIS_17_in_Naples//abstracts" target="_blank" >http://www.enbis.org/activities/events/current/534_ENBIS_17_in_Naples//abstracts</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Semiparametric statistical analysis of the Blade Tip Timing data for detection of turbine rotor speed instabilities.

Popis výsledku v původním jazyce

ENBIS-17 in Naples (Italy), 9.-14. 9. 2017, European Network for Business and Industrial Statistics. We will present a semiparametric statistical model for detecting instabilities in a turbine rotor speed. The modeling and detection uses data obtained from the now standard BTT (Blade Tip Timing) contactless measurement method. The model is based on time-varying coefficient model formulated as a GAM (Generalized Additive Model) with appropriately selected penalty. Our approach can be perceived as a fully formalized time-varying statistical extension of the traditional Fourier analysis. As such, it can reveal important rotor instabilities not readily apparent in the traditional approaches. After presenting the underlying statistical modeling framework, we will illustrate the performance of our methodology on experimental data measured on a test turbine via magneto-resistive BTT technology. The research is supported from the AV21 Strategy of the Academy of Sciences of the Czech Republic.

Název v anglickém jazyce

Semiparametric statistical analysis of the Blade Tip Timing data for detection of turbine rotor speed instabilities.

Popis výsledku anglicky

ENBIS-17 in Naples (Italy), 9.-14. 9. 2017, European Network for Business and Industrial Statistics. We will present a semiparametric statistical model for detecting instabilities in a turbine rotor speed. The modeling and detection uses data obtained from the now standard BTT (Blade Tip Timing) contactless measurement method. The model is based on time-varying coefficient model formulated as a GAM (Generalized Additive Model) with appropriately selected penalty. Our approach can be perceived as a fully formalized time-varying statistical extension of the traditional Fourier analysis. As such, it can reveal important rotor instabilities not readily apparent in the traditional approaches. After presenting the underlying statistical modeling framework, we will illustrate the performance of our methodology on experimental data measured on a test turbine via magneto-resistive BTT technology. The research is supported from the AV21 Strategy of the Academy of Sciences of the Czech Republic.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20302 - Applied mechanics

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů