Academic Staff

 

Brief Biosketch

Dr. Liqiao Xia is currently a Research Assistant Professor in the Department of Industrial and Systems Engineering at The Hong Kong Polytechnic University, HKSAR. Before this, he was a Visiting Research Fellow at the Vienna University of Technology, Austria, and the Karlsruhe Institute of Technology, Germany, and a Postdoctoral Scholar in the Department of Mechanical and Aerospace Engineering at Case Western Reserve University, USA. He received his Ph.D. in Industrial and Systems Engineering from The Hong Kong Polytechnic University, HKSAR, in 2024 and was a Visiting Graduate Student at the Institute for Manufacturing, University of Cambridge, UK. His research interests include human-robot collaboration, smart manufacturing systems, prognostics and health management, etc. He has published numerous papers in prestigious journals, such as IEEE Trans. (TII, TMECH, TIM, TR, TBD) and RESS, among others. 

 

Research Interests

• Human-Robot Collaboration
• Smart Manufacturing System
• Prognostics and Health Management
• Industrial AI

 

Selected Journal Publications

1. L. Xia, Y. Hu, J. Pang, X. Zhang and C. Liu, "Leveraging Large Language Models to Empower Bayesian Networks for Reliable Human-Robot Collaborative Disassembly Sequence Planning in Remanufacturing," IEEE Transactions on Industrial Informatics, vol. 21, no. 4, pp. 3117-3126, 2025.
2. L. Xia, J. Fan, A. Parlikad, X. Huang, and P. Zheng, “Unlocking large language model power in industry: Privacy- preserving collaborative creation of knowledge graph,” IEEE Transactions on Big Data.
3. L. Xia, P. Zheng, M. Herrera, Y. Liang, X. Li and L. Gao, "Graph Embedding-Based Bayesian Network for Fault Isolation in Complex Equipment," IEEE Transactions on Reliability, doi: 10.1109/TR.2024.3416064
4. L. Xia, C. Li, C. Zhang, S. Liu, and P. Zheng, “Leveraging error-assisted fine-tuning large language models for manufacturing excellence,” Robotics and Computer-Integrated Manufacturing, vol. 88, p. 102 728, 2024.
5. L. Xia, Y. Liang, J. Leng, and P. Zheng, “Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network,” Reliability Engineering & System Safety, vol. 232, p. 109 068, 2023, (ESI Highly cited paper).
6. L. Xia, P. Zheng, J. Li, X. Huang, and R. X. Gao, “Histogram-based gradient boosting tree: A federated learning approach for collaborative fault diagnosis,” IEEE/ASME Transactions on Mechatronics, 2023.
7. L. Xia, P. Zheng, X. Li, R. X. Gao, and L. Wang, “Toward cognitive predictive maintenance: A survey of graph-based approaches,” Journal of Manufacturing Systems, vol. 64, pp. 107–120, 2022.
8. L. Xia, P. Zheng, X. Huang, and C. Liu, “A novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarization,” Journal of Intelligent Manufacturing, vol. 33, no. 8, pp. 2295–2306, 2022.
9. L. Xia, Y. Liang, P. Zheng, and X. Huang, “Residual-hypergraph convolution network: A model-based and data- driven integrated approach for fault diagnosis in complex equipment,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–11, 2022.
10. L. Xia, P. Zheng, J. Li, W. Tang, and X. Zhang, “Privacy-preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis,” IET Collaborative Intelligent Manufacturing, vol. 4, no. 3, pp. 208–219, 2022.