Research progress of industrial robot fault diagnosis based on deep learning

In light of the challenges posed by the low efficiency and accuracy of conventional industrial robot fault diagnosis methods, industrial robot fault diagnosis methods based on deep learning have become a current research hot spot. This paper will focus on the research progress of convolutional neural networks, deep belief networks, generative adversarial networks, and other models of deep learning in industrial robot fault diagnosis, and analyze the future development trend of deep learning in industrial robot fault diagnosis more precisely, aiming to better provide methodological guidance for the subsequent research of industrial robot fault diagnosis.

1 Introduction

With the rapid advancement of modern intelligent manufacturing technology, industrial robots have become indispensable tools for enhancing automation levels in manufacturing production. However, the growing complexity, exorbitant maintenance costs, and prolonged downtime associated with industrial robots impose significant burdens on enterprises. The occurrence of failures not only disrupts normal operations but also leads to substantial economic losses. As a result, ensuring the safety and reliability of industrial robots emerges as a pivotal challenge in their development. Consequently, the establishment of a comprehensive industrial robot fault diagnosis system assumes utmost significance, driving forward the advancement of industrial robot technology.