Deep learning

Automation

Transfer of learning

Mobile robot

Pure mathematics

Technology transfer

Mathematics

Knowledge management

Algorithm

Machine learning

Engineering

Artificial intelligence

Robot learning

Computer science

Field (mathematics)

State (computer science)

Inductive transfer

Mechanical engineering

Robot

IEEE Industrial Electronics Magazine

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.

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