| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 495264 | Applied Soft Computing | 2015 | 9 Pages |
•We address the problem of missing data in multidimensional time series.•We propose a novel method based on a fuzzy similarity measure.•The performance is compared with that of an Auto Associative Kernel Regression.•The method is applied to shut-down transients of a Nuclear Power Plant (NPP) turbine.
The present work addresses the problem of missing data in multidimensional time series such as those collected during operational transients in industrial plants. 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 compared with that of an Auto Associative Kernel Regression (AAKR) method on an artificial case study and a real industrial application regarding shut-down transients of a Nuclear Power Plant (NPP) turbine.
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