Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Regularization-based Continual Learning for Fault Prediction in
  Lithium-Ion Batteries   In recent years, the use of lithium-ion batteries has greatly expanded into
products from many industrial sectors, e.g. cars, power tools or medical
devices. An early prediction and robust understanding of battery faults could
therefore greatly increase product quality in those fields. While current
approaches for data-driven fault prediction provide good results on the exact
processes they were trained on, they often lack the ability to flexibly adapt
to changes, e.g. in operational or environmental parameters. Continual learning
promises such flexibility, allowing for an automatic adaption of previously
learnt knowledge to new tasks. Therefore, this article discusses different
continual learning approaches from the group of regularization strategies,
which are implemented, evaluated and compared based on a real battery wear
dataset. Online elastic weight consolidation delivers the best results, but, as
with all examined approaches, its performance appears to be strongly dependent
on task characteristics and task sequence.

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The categories of this paper are: q-alg
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: cond-mat
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The categories of this paper are: eess
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The categories of this paper are: alg-geom