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Communication Dans Un Congrès Année : 2013

A Fuzzy Similarity Based Method for Signal Reconstruction during Plant Transients

Résumé

We consider the problem of missing data in the context of on-line condition monitoring of industrial components by empirical, data-driven models. We propose a novel method for missing data reconstruction based on three main steps: (1) computing a fuzzy similarity measure between a segment of the time series containing the missing data and segments of reference time series; (2) assigning a weight to each reference segment; (3) reconstructing the missing values as a weighted average of the reference segments. The performance of the proposed method is verified on a real industrial application regarding shut-down transients of a Nuclear Power Plant (NPP) turbine.
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Dates et versions

hal-00926901 , version 1 (10-01-2014)

Identifiants

  • HAL Id : hal-00926901 , version 1

Citer

Piero Baraldi, Francesco Di Maio, Davide Genini, Enrico Zio. A Fuzzy Similarity Based Method for Signal Reconstruction during Plant Transients. Prognostics and System Health Management Conference, Oct 2013, Italy. pp.1-6. ⟨hal-00926901⟩
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