Papers
1
Total Citations
18
H-Index
1
About
Dr. Zihao Zang is a leading researcher in intelligent manufacturing and industrial robotics, with a primary focus on fault diagnosis and multi-source data fusion. His most cited work, “Fault Diagnosis of Industrial Robot Based on Multi-Source Data Fusion and Channel Attention Convolutional Neural Networks” (2024, 18 citations), addresses a critical challenge in smart factories: the degradation of robot movement accuracy due to harsh operating environments. By integrating channel attention mechanisms with convolutional neural networks, Dr. Zang developed a novel diagnostic framework that outperforms traditional single-source methods, enabling more reliable and proactive robot maintenance. His research bridges the gap between data-driven AI and practical industrial applications, offering scalable solutions for collaborative robot health management. Dr. Zang’s contributions are particularly impactful for the growing field of Industry 4.0, where equipment reliability is paramount. His work has been recognized for its potential to reduce downtime and improve production efficiency, marking him as an emerging authority in intelligent fault diagnosis and multi-modal sensor fusion for robotic systems.
Research Focus
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Top Papers
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