Integrating transfer learning within data-driven soft sensor design to accelerate product quality control

Abstract

The measurement of batch quality indicators in real time operation is plagued with many challenges, hence soft sensing has become a promising solution within industrial research. However, small data has traditionally been a severe problem, hindering the ability to create accurate, reliable soft sensors, especially within industrial research and development for new product formulations. Nevertheless, it is often the case that modelling knowledge is available for a related system. In order to exploit this, we have developed a generalisable transfer learning methodology which takes advantage of previous modelling efforts to accelerate and improve the construction of models for new systems. Specifically, we adapted a recently developed advanced data-driven soft sensing methodology made for an existing process formulation and integrated a feature-based transfer learning approach to facilitate the modelling of two new industrial process systems, each of which containing notable differences to the original. The performance of the transfer soft sensors was tested rigorously and compared to a benchmark approach under different data availability conditions. It was shown that, the proposed transfer mechanism yielded high accuracy, and is robust to small data scenarios, indicating its potential for use in soft sensing of novel systems.