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Research on Intelligent Fault Diagnosis and Self-Repair Mechanism of Industrial Robots

Author(s)

Wanting Xu

Affiliation(s)

‌Jilin Engineering Normal University, Changchun, Jilin, China

Abstract

With the rapid development of industrial automation, industrial robots are increasingly widely used in production lines, and their stability and reliability are directly related to production efficiency and cost. This paper focuses on the research of intelligent fault diagnosis and self-repair mechanism of industrial robots, and constructs an intelligent fault diagnosis model by comprehensively using machine learning, sensor technology and data fusion algorithm. Through multi-source sensors to collect real-time robot operation data, after feature extraction and data preprocessing input model, to achieve accurate diagnosis of faults. Meanwhile, based on the diagnostic results, a self-repair mechanism is designed, covering hardware redundancy switching, software parameter adaptive adjustment, and automatic replacement strategy for faulty parts, which effectively improves the robot's fault coping ability and autonomous operation level, and reduces the downtime. Experiments show that the proposed method has a fault diagnosis accuracy of 97.3% in typical industrial scenarios, and the average repair response time is shortened to 1.5 seconds, which reduces the downtime loss by about 40% compared with the traditional method, and provides a strong guarantee for the continuity and stability of industrial production.

Keywords

Industrial Robot; Intelligent Fault Diagnosis; Self-Repair; Multi-Source Data Fusion; Machine Learning

References

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