Ershun Pan
Papers
3
Total Citations
17
H-Index
3
About
Ershun Pan is a leading researcher in intelligent manufacturing and industrial robotics, with a focus on reliability engineering, predictive maintenance, and anomaly detection. His work addresses critical challenges in automated production systems, particularly in no-wait manufacturing environments like hot rolling, where he pioneered system-level predictive maintenance optimization under economic dependency and hybrid fault modes. This research, cited 6 times, introduces opportunistic maintenance strategies that leverage breaks between production batches to enhance machine-robot collaboration. Pan also developed a dynamic reliability assessment framework for industrial robot motion stability using high-order response moments (4 citations), enabling more accurate performance predictions. Most notably, his recent work on unsupervised motion-based anomaly detection with graph attention networks (7 citations) provides a groundbreaking approach for labeling industrial robot anomalies without labeled training data. By integrating graph neural networks with motion analysis, Pan has advanced real-time monitoring and fault diagnosis in smart factories. His contributions bridge theoretical reliability models with practical industrial applications, making him a key figure in the evolution of autonomous manufacturing systems.
Research Focus
Key Achievements
Top Papers
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