A Fuzzy Similarity Based Method for Signal Reconstruction during Plant Transients

Abstract

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.