#5757. Unstructured borderline self-organizing map: Learning highly imbalanced, high-dimensional datasets for fault detection

July 2026publication date
Proposal available till 11-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Engineering (all);
Computer Science Applications;
Artificial Intelligence;
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Abstract:
Fault detection in industrial processes is critical for yield improvement and manufacturing cost reduction. However, most industrial processes produce highly imbalanced and high-dimensional datasets, in which the normal data overwhelm the fault data in number and many noninformative features add noise to the data distribution. Thus, addressing class imbalance and high-dimensionality problems has been considered key to successful fault detection. In this paper, we propose a novel model called an unstructured borderline self-organizing map (UB-SOM) designed to solve these two problems. UB-SOM not only learns the distribution of the normal samples through a small number of representative nodes but also highlights borderline areas. Since UB-SOM yields a new data distribution that emphasizes borderlines, the distributional change from the normal data to the representative nodes reveals which features are considered significant in the borderline areas. We select the significant features based on the featurewise distributional change measured using the Kullback-Leibler divergence.
Keywords:
Borderline; Fault detection; Feature selection; Industrial processes; Resampling; Self-organizing map

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