Aidong Xu
Papers
2
Total Citations
71
H-Index
2
About
Aidong Xu is a leading researcher in intelligent fault diagnosis and smart manufacturing, with a primary focus on enhancing the reliability of industrial robots. Dr. Xu’s work addresses critical challenges in automated systems, particularly the diagnosis of joint bearing faults—a major cause of robotic failures. Their most impactful contribution, the 2022 paper on "Intelligent Fault Diagnosis for Bearings of Industrial Robot Joints Under Varying Working Conditions Based on Deep Adversarial Domain Adaptation," has garnered 65 citations and pioneered the use of deep adversarial learning to overcome data distribution shifts in real-world manufacturing environments. This work enables robust diagnosis across different operating conditions, a key hurdle for practical deployment. Additionally, Dr. Xu has advanced the field by tackling the pervasive problem of imbalanced data, where normal operational data vastly outnumbers fault data. Their 2022 study on the "Multiclass Mahalanobis-Taguchi System for Imbalanced Data" offers a novel solution to prevent diagnostic bias toward majority categories. By combining deep learning with statistical methods, Aidong Xu’s research directly improves the safety and efficiency of industrial automation, making their work essential reading for engineers and researchers in prognostics and health management.
Research Focus
Key Achievements
Top Papers
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