Related papers: A deep latent variable model for semi-supervised multi-unit soft sensing in industrial processes

A deep latent variable model for semi-supervised multi-unit soft sensing in industrial processes

URL: http://arxiv.org/abs/2407.13310v1
Date: Thu, 18 Jul 2024 09:13:22 GMT
Title: A deep latent variable model for semi-supervised multi-unit soft sensing in industrial processes
Authors: Bjarne Grimstad, Kristian Løvland, Lars S. Imsland, Vidar Gunnerud,
Abstract summary: We introduce a deep latent variable model for semi-supervised multi-unit soft sensing. This hierarchical, generative model is able to jointly model different units, as well as learning from both labeled and unlabeled data. We show that by combining semi-supervised and multi-task learning, the proposed model achieves superior results.
Score: 0.0
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: In many industrial processes, an apparent lack of data limits the development of data-driven soft sensors. There are, however, often opportunities to learn stronger models by being more data-efficient. To achieve this, one can leverage knowledge about the data from which the soft sensor is learned. Taking advantage of properties frequently possessed by industrial data, we introduce a deep latent variable model for semi-supervised multi-unit soft sensing. This hierarchical, generative model is able to jointly model different units, as well as learning from both labeled and unlabeled data. An empirical study of multi-unit soft sensing is conducted using two datasets: a synthetic dataset of single-phase fluid flow, and a large, real dataset of multi-phase flow in oil and gas wells. We show that by combining semi-supervised and multi-task learning, the proposed model achieves superior results, outperforming current leading methods for this soft sensing problem. We also show that when a model has been trained on a multi-unit dataset, it may be finetuned to previously unseen units using only a handful of data points. In this finetuning procedure, unlabeled data improve soft sensor performance; remarkably, this is true even when no labeled data are available.

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