Meng Xiong
Papers
2
Total Citations
7
H-Index
2
About
Meng Xiong is a researcher at the forefront of intelligent industrial robotics, specializing in fault diagnosis and prognostics and health management (PHM) for multi-axis systems. His work bridges deep learning and natural language processing to enhance robotic reliability and maintenance. Xiong’s most notable contribution is a fault diagnosis model based on an improved one-dimensional convolutional neural network (1D-CNN), which achieves high accuracy in identifying mechanical anomalies in complex industrial robots. This model, cited five times, offers a practical, data-driven solution for real-time monitoring. He further advanced the field by developing a hybrid entity-relation extraction method using BiLSTM-CRF and multi-head selection, enabling automated knowledge extraction from unstructured maintenance logs—a critical step toward intelligent PHM systems. Though his citation counts are modest, Xiong’s work is foundational for integrating AI into industrial robot health management, with potential to reduce downtime and maintenance costs. His research reflects a growing trend toward interpretable, efficient deep learning models for manufacturing, positioning him as a rising contributor to smart factory technologies.
Research Focus
Key Achievements
Top Papers
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