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

TL;DR

This paper introduces a deep latent variable model for semi-supervised multi-unit soft sensing in industrial processes, effectively leveraging labeled and unlabeled data to improve sensor accuracy across different units.

Contribution

The paper presents a hierarchical generative model that jointly models multiple units and learns from both labeled and unlabeled data, enhancing soft sensor performance in industrial settings.

Findings

Outperforms existing methods in multi-unit soft sensing tasks.

Enables effective finetuning on unseen units with minimal data.

Unlabeled data significantly improve sensor accuracy even without labels.

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.