Multi-unit soft sensing permits few-shot learning
Bjarne Grimstad Kristian Løvland Lars S. Imsland
Recent literature has explored various ways to improve soft sensors using
learning algorithms with transferability. Broadly put, the performance of a
soft sensor may be strengthened when it is learned by solving multiple tasks.
The usefulness of transferability depends on how strongly related the devised
learning tasks are. A particularly relevant case for transferability, is when a
soft sensor is to be developed for a process of which there are many
realizations, e.g. system or device with many implementations from which data
is available. Then, each realization presents a soft sensor learning task, and
it is reasonable to expect that the different tasks are strongly related.
Applying transferability in this setting leads to what we call multi-unit soft
sensing, where a soft sensor models a process by learning from data from all of
its realizations.
  This paper explores the learning abilities of a multi-unit soft sensor, which
is formulated as a hierarchical model and implemented using a deep neural
network. In particular, we investigate how well the soft sensor generalizes as
the number of units increase. Using a large industrial dataset, we demonstrate
that, when the soft sensor is learned from a sufficient number of tasks, it
permits few-shot learning on data from new units. Surprisingly, regarding the
difficulty of the task, few-shot learning on 1-3 data points often leads to a
high performance on new units.