Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning

TL;DR

This paper reviews recent advances in deep transfer learning for industrial automation, highlighting the integration of transfer and continual learning to develop robust, data-driven machine learning methods across various industrial applications.

Contribution

It introduces the concepts of transfer and continual learning, reviews promising approaches in industrial deep transfer learning, and advocates combining these methods for improved industrial automation solutions.

Findings

Transfer learning is state-of-the-art in computer vision.

Application in fault prediction is still emerging.

Unified approach can enhance robustness in industrial settings.

Abstract

In this article, the concepts of transfer and continual learning are introduced. The ensuing review reveals promising approaches for industrial deep transfer learning, utilizing methods of both classes of algorithms. In the field of computer vision, it is already state-of-the-art. In others, e.g. fault prediction, it is barely starting. However, over all fields, the abstract differentiation between continual and transfer learning is not benefitting their practical use. In contrast, both should be brought together to create robust learning algorithms fulfilling the industrial automation sector's requirements. To better describe these requirements, base use cases of industrial transfer learning are introduced.