Transfer learning for servomotor bearing fault detection in the industrial robot

Abstract

In consequence of their superior performance and durability, industrial robots have enjoyed widespread adoption across a variety of industries. However, despite their sturdy build, they are susceptible to malfunction. The servomotor is a fundamental component of industrial robots, and to ensure smooth and uninterrupted functioning, it is essential to detect any defects it may develop. Although research has addressed methods for detecting bearing failure, diagnosis of a servomotor bearing failure in the industrial robot remains difficult and requires intensive research. In this paper, a novel method for detecting servomotor bearing defects in the industrial robot is provided by integrating knowledge transfer via transfer learning. Initially, current signals of the servomotor are transformed to scalogram images. This processed data is utilized to build the model for fault detection. Applying transfer learning eliminates model training from scratch and streamlined operations. The purported approach shows an average accuracy of more than 99 %.