Multi-unit soft sensing permits few-shot learning

• URL: http://arxiv.org/abs/2309.15828v2
• Date: Mon, 13 May 2024 08:50:14 GMT
• Title: Multi-unit soft sensing permits few-shot learning
• Authors: Bjarne Grimstad, Kristian Løvland, Lars S. Imsland,
• Abstract summary: A performance gain is generally attained when knowledge is transferred among strongly related soft sensor learning tasks. A particularly relevant case for transferability is when developing soft sensors of the same type for similar, but physically different processes or units. Applying methods that exploit transferability in this setting leads to what we call multi-unit soft sensing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent literature has explored various ways to improve soft sensors by utilizing learning algorithms with transferability. A performance gain is generally attained when knowledge is transferred among strongly related soft sensor learning tasks. A particularly relevant case for transferability is when developing soft sensors of the same type for similar, but physically different processes or units. Then, the data from each unit presents a soft sensor learning task, and it is reasonable to expect strongly related tasks. Applying methods that exploit transferability in this setting leads to what we call multi-unit soft sensing. This paper formulates multi-unit soft sensing as a probabilistic, hierarchical model, which we implement using a deep neural network. The learning capabilities of the model are studied empirically on a large-scale industrial case by developing virtual flow meters (a type of soft sensor) for 80 petroleum wells. We investigate how the model generalizes with the number of wells/units. Interestingly, we demonstrate that multi-unit models learned from data from many wells, permit few-shot learning of virtual flow meters for new wells. Surprisingly, regarding the difficulty of the tasks, few-shot learning on 1-3 data points often leads to high performance on new wells.

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