[3B1] A concise review of transfer learning and generative learning for autonomous and robotic systems fault detection and diagnosis
C Li and L Zhang
University of Manchester, UK
In autonomous and robotic systems, the importance of fault detection and diagnosis (FDD)
technologies has increasingly grown as these systems find widespread application across various
sectors. There are a wide range of data-driven machine learning approaches for FDD. These
methods often require large amounts of normal and fault data for model training. In scenarios
with limited data availability, transfer learning boosts learning efficiency by transferring prior
knowledge. In contrast, generative learning, such as generative adversarial networks (GANs),
enhances the accuracy and generalisation capabilities of fault diagnosis models by generating
simulated fault data. While several reviews focus on component-specific fault analysis via transfer
learning and generative learning, such as bearings and gearboxes, research on system-level fault
diagnosis is comparatively scarce. In this context, the aim of this paper is to review the application
of transfer learning and generative learning in FDD technologies for autonomous and robotic
systems. The article expands the research scope of FDD from individual components to entire
systems. It explores the potential value and challenges of two learning strategies in advancing
FDD technology. Finally, it presents new perspectives and directions for future research in this
field.
Keywords: fault detection and diagnosis, autonomous and robotic systems, transfer learning, generative learning, system-level fault diagnosis.
Keywords: fault detection and diagnosis, autonomous and robotic systems, transfer learning, generative learning, system-level fault diagnosis.
