کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
297676 511763 2011 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Signal reconstruction by a GA-optimized ensemble of PCA models
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی مهندسی انرژی و فناوری های برق
پیش نمایش صفحه اول مقاله
Signal reconstruction by a GA-optimized ensemble of PCA models
چکیده انگلیسی

On-line sensor monitoring allows detecting anomalies in sensor operation and reconstructing the correct signals of failed sensors by exploiting the information coming from other measured signals. In field applications, the number of signals to be monitored is often too large to be handled effectively by a single reconstruction model. A more viable approach is that of decomposing the problem by constructing a number of reconstruction models, each one handling an individual group of signals. To apply this approach, two problems must be solved: (1) the optimal definition of the groups of signals and (2) the appropriate combination of the outcomes of the individual models. With respect to the first problem, in this work, Multi-Objective Genetic Algorithms (MOGAs) are devised for finding the optimal groups of signals used for building reconstruction models based on Principal Component Analysis (PCA). With respect to the second problem, a weighted scheme is adopted to combine appropriately the signal predictions of the individual models. The proposed approach is applied to a real case study concerning the reconstruction of 84 signals collected from a Swedish nuclear boiling water reactor.

Research highlights▶ Large-scale signal reconstruction requires the adoption of a multi-model ensemble approach. ▶ Genetic Algorithms are a powerful tool to optimize the generation of the models. ▶ Principal Component Analysis is a fast and accurate reconstruction model. ▶ An ensemble aggregation approach with robust training provides high reconstruction robustness.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Nuclear Engineering and Design - Volume 241, Issue 1, January 2011, Pages 301–309
نویسندگان
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