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Semiparametric Statistical Analysis of the Blade Tip Timing Data for Detection of Turbine Rotor Speed Instabilities
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SYSNO ASEP 0490148 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Semiparametric Statistical Analysis of the Blade Tip Timing Data for Detection of Turbine Rotor Speed Instabilities Author(s) Brabec, Marek (UIVT-O) RID, SAI, ORCID
Procházka, Pavel (UT-L) RID, ORCID
Maturkanič, Dušan (UT-L)Source Title Quality and Reliability Engineering International. - : Wiley - ISSN 0748-8017
Roč. 34, č. 7 (2018), s. 1308-1314Number of pages 7 s. Language eng - English Country GB - United Kingdom Keywords BTT ; GAM ; semiparametric model ; state-space model ; vibrodiagnostics ; turbine rotor ; rotating speed instability ; non-contact vibration diagnostics ; time-varying-coefficient model Subject RIV BB - Applied Statistics, Operational Research OECD category Statistics and probability Subject RIV - cooperation Institute of Thermomechanics - Sensors, Measurment, Regulation Institutional support UIVT-O - RVO:67985807 ; UT-L - RVO:61388998 UT WOS 000445334700002 EID SCOPUS 85053641718 DOI 10.1002/qre.2327 Annotation In this paper, we propose an extension of an existing standard approach to the blade tip timing data when analyzing turbine rotor vibrations. Instabilities related to the non‐constant trigonometric coefficients at prominent frequencies might not be noticed in the traditional analyses. Our methodology is based on time‐varying coefficient statistical models, and hence it allows a full formalization of the estimation and other inferential tasks (uncertainty assessments, hypothesis tests etc.). First, we formulate a univariate generalized additive model that is useful for investigation of vibration behavior of individual blades. It can extract trajectories of the trigonometric coefficients. Using the trajectories, one can investigate time changes of power at a given frequency. In the second approach, we use a multivariate model for simultaneous assessment of all of the rotor blades. The model acknowledges similarity of the vibration behavior of closely located blades. It is formulated as a state‐space model, and hence it allows for a wide range of prediction, smoothing, and filtering tasks. We illustrate the performance and practical usefulness of our models on real blade tip timing turbine monitoring data obtained from the Czech nuclear power plant Temelin. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2019
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