Deep transfer learning methods for typical supervised tasks in industrial monitoring: state-of-the-art, challenges, and perspectives

Abstract

Deep transfer learning-based industrial monitoring methods have received considerable research attention in recent years, especially in typical industrial monitoring tasks, including fault diagnosis and soft sensor developments. Such methods mine and transfer knowledge from similar source domains to model the data in the target domain. They provide a new perspective for cross-domain industrial monitoring problems caused by varying conditions in actual scenarios. This survey systematically sorts the deep transfer learning methods for typical supervised tasks in industrial monitoring and classifies them into model-based, instance-based, and feature-based approaches. Subsequently, it introduces the basic ideas and state-of-the-art approaches in fault diagnosis and soft sensor development of different categories. Finally, from the perspectives of complexly limited data, evaluation of transferability and negative transfer problems, and the dynamic characteristics of industrial processes, the survey highlights the current challenges in cross-domain industrial monitoring and points to future research areas in this field.