Transfer learning based cross-process fault diagnosis of industrial robots

Author:

Wang Junchi1,Xiao Hong1,Jiang Wenchao1,Li Ping1,Li Zelin1,Wang Tao2

Affiliation:

1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China

2. School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Abstract

In the actual industrial application of robots, the characteristics of robot malfunctions change accordingly as the working environment becomes increasingly diverse and complex. Utilizing the original fault diagnosis models in new working environments correspondingly leads to a decline in the performance and the generalization capability of the model. Moreover, the monitoring data collected in new working processes often has limited or no labels, making the diagnosis models trained with this data unable to identify faults accurately. In this paper, we propose a Domain adaptive Cross-process Fault Diagnosis method (DCFD) to leverage knowledge from existing working processes for diagnosing faults in new working processes. DCFD uses Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to measure the difference between the current working processes and the previous working processes, enhancing the fault diagnosis capability of the robotic system in cross-process scenarios. DCFD achieves an average fault classification accuracy of 98% on 12 types of migration tasks, which demonstrates the effectiveness of DCFD on cross-process fault diagnosis classification tasks in real-time industrial application scenarios.

Publisher

IOS Press

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimized Real-Time Monitoring and Fault Diagnosis System for Industrial Robots with Integrated Sensor Data;2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN);2024-07-03

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