Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection
This work addresses the challenge of improving transfer learning efficiency in industrial settings, but it is incremental as it applies an existing clustering method to a specific domain.
The paper tackles the problem of selecting appropriate cases for industrial transfer learning by proposing a clustering-based method, specifically using the BIRCH algorithm, and demonstrates its applicability with reproducible results on an industrial time series dataset from discrete manufacturing.
Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts. The appropriate selection of what to transfer can vastly improve a transfer's results. In this paper, a transfer case selection based upon clustering is presented. Founded on a survey of clustering algorithms, the BIRCH algorithm is selected for this purpose. It is evaluated on an industrial time series dataset from a discrete manufacturing scenario. Results underline the approaches' applicability caused by its results' reproducibility and practical indifference to sequence, size and dimensionality of (sub-)datasets to be clustered sequentially.