Research on Fault Diagnosis Method of Joint Bearing of Industrial Robot Based on Digital Twin and ResTLN Fusion

Industrial robots are indispensable equipment in automated production lines and play a crucial role in advancing the development of intelligent manufacturing. Bearings are key components within robot joints. To ensure the precise execution of operational tasks and to prevent potential safety accidents in a timely manner, it is essential to perform fault diagnosis on the bearings within robot joints. However, fault diagnosis methods based on deep learning typically require a large amount of fault measurement data, which can be challenging to obtain due to various constraints. To address the issue of insufficient data, this paper proposes a fault diagnosis method based on the integration of digital twin technology and MTF-ResTLN. First, a digital twin model of the industrial robot is established, and fault excitations are injected into different nodes of the twin model to generate fault data under various node conditions. The measured data are then combined with the simulated fault data to form a training dataset. Furthermore, a novel classifier is developed by integrating the Markov Transition Field with a Residual Transfer Learning Network. It achieves cross-domain fault diagnosis and enhances the capability of fault diagnosis.