一种基于数字孪生的工业机器人故障预测与健康管理系统
By organizing and analyzing robot operation data using digital twin technology, generating a twin baseline set and performing parameter inversion, the problem of lack of verification after maintenance in existing technologies is solved, realizing the consistency verification of parameters before and after maintenance, and improving the accuracy and efficiency of fault prediction and health management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHONGHAICHENG (BEIJING) TECHNOLOGY CO LTD
- Filing Date
- 2026-04-14
- Publication Date
- 2026-07-17
AI Technical Summary
Existing industrial robot fault prediction and health management systems lack a consistency verification mechanism between predicted root causes before maintenance and parameter changes after maintenance, resulting in a disconnect between the maintenance link and the fault prediction link, leading to frequent occurrences of incorrect maintenance and repeated downtime.
A fault prediction and health management system based on digital twins is adopted. Through data processing, prediction modeling, parameter inversion, and falsification and write-back modules, the joint current, temperature, error, and alarm event data of industrial robots are processed and analyzed to generate a twin baseline set, perform parameter inversion and consistency judgment, form falsification results, and update the knowledge set.
It enables the re-verification and feedback of pre-maintenance assessment results, accurately identifies fault sources and corrects maintenance strategies, reduces mis-repairs and repeated downtime, improves the accuracy of fault diagnosis, and reduces maintenance costs.
Smart Images
Figure CN122033995B_ABST