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

0482024 - ÚT 2018 RIV IT eng A - Abstract
Brabec, Marek - Procházka, Pavel - Maturkanič, Dušan
Semiparametric statistical analysis of the Blade Tip Timing data for detection of turbine rotor speed instabilities.
ENBIS-17 in Naples. ENBIS, 2017.
[ENBIS-17 (European Network for Business and Industrial Statistics). 09.09.2017-14.09.2017, Naples]
Grant - others:AV ČR(CZ) StrategieAV21/3
Program: StrategieAV
Institutional support: RVO:61388998 ; RVO:67985807
Keywords : GAM * BTT * time-varying model * semiparametric model
OECD category: Applied mechanics; Statistics and probability (UIVT-O)
http://www.enbis.org/activities/events/current/534_ENBIS_17_in_Naples//abstracts

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.
Permanent Link: http://hdl.handle.net/11104/0279650