Shandong University of Science and Technology | China
Dr. Fucan Huang is an emerging researcher in mechanical fault diagnosis, known for advancing intelligent diagnostic methods through deep learning, multi-attention mechanisms, and cross-domain adaptability. His work focuses on improving fault detection accuracy in complex industrial environments, particularly under fluctuating conditions, imbalanced datasets, and multi-source domain challenges. He has contributed significantly to Measurement Science and Technology and Machines, co-authoring several influential papers. His research includes the development of an adaptive attenuation self-attention adversarial network for cross-domain diagnosis, a multi-source domain collaborative bearing fault method guided by multi-attention mechanisms, and innovative dual-domain vision transformer frameworks. These contributions enhance the robustness, generalizability, and interpretability of diagnostic models, strengthening intelligent maintenance systems and promoting safer, more efficient industrial operations.
Profile : Orcid
Featured Publications
An, Y., Zhang, D., Zhang, M., Xin, M., Wang, Z., Ding, D., Huang, F., & Wang, J. (2025). Residual attention-driven dual-domain vision transformer for mechanical fault diagnosis. Machines, 13(12), Article 1096.
Huang, F., Zhang, Q., Han, B., Wang, J., Zhang, Z., Ge, R., & Gong, H. (2025). Adaptive attenuation self-attention adversarial network for cross-domain fault diagnosis under imbalanced conditions. Measurement Science and Technology, 31 December 2025.
Ge, R., Zhang, Z., Wang, J., Wang, W., Fan, Z., & Huang, F. (2025). Multi-source domain bearing fault collaborative diagnosis method for unbalanced samples guided by multi-attention mechanism. Measurement Science and Technology, 30 November 2025.
Han, B., Huang, F., Qin, M., Qin, H., Wang, J., Zhang, Z., & Yu, Y. (2025). Dual-domain fused vision transformer for mechanical fault diagnosis under fluctuating working conditions. Measurement Science and Technology, 30 April 2025.