Dr. Zhen Guo | Engineering | Best Researcher Award
Zhen Guo is a doctoral student at the School of Transportation and Logistics Engineering, Wuhan University of Technology. His research focuses on deep learning, fault diagnosis, and intelligent monitoring of rotating machinery. He specializes in anomaly detection, imbalance learning, few-shot learning, and transfer learning, applying these methods to improve reliability in robotics and mechanical systems. Zhen has served as a reviewer for several high-impact journals and conferences, reflecting his active engagement in the research community and his expertise in intelligent diagnostics and engineering applications.
Education
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Zhen Guo is currently pursuing doctoral studies at Wuhan University of Technology in the School of Transportation and Logistics Engineering, where his research is focused on cutting-edge developments in intelligent systems and mechanical diagnostics. Prior to this, he completed his master’s studies at Zhengzhou University of Light Industry, laying a strong foundation in engineering and applied technologies. His academic path demonstrates a consistent focus on integrating deep learning with real-world engineering challenges.
Experience
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Zhen has actively contributed to the academic community as a peer reviewer for several internationally recognized journals and conferences. His review work spans prestigious platforms such as Advanced Engineering Informatics, Knowledge-Based Systems, Engineering Applications of Artificial Intelligence, ISA Transactions, Measurement, Measurement Science and Technology, Journal of Mechanical Science and Technology, Engineering Research Express, and Scientific Reports. His peer review contributions highlight his depth of knowledge and his commitment to advancing research quality in the fields of mechanical systems and artificial intelligence.
Awards and Recognition
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Zhen’s selection as a reviewer for top-tier journals and conferences serves as a testament to his expertise and standing within the academic community. This level of involvement often reflects recognition by peers and editors for his technical insight and critical thinking.
Skills and Certifications
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Zhen’s core competencies include deep learning, fault diagnosis, robotics, rotating machinery analysis, anomaly detection, and imbalance learning. He is particularly skilled in emerging machine learning paradigms such as few-shot learning and transfer learning. His research integrates these advanced techniques to solve complex problems related to industrial automation and intelligent monitoring.
Research Focus
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Zhen Guo’s research centers on the intersection of artificial intelligence and mechanical engineering, with an emphasis on fault diagnosis and intelligent monitoring systems. He is especially interested in leveraging deep learning for detecting anomalies in rotating machinery, optimizing performance through imbalance learning, and developing robust solutions using few-shot and transfer learning approaches. His work contributes to building smarter, more reliable engineering systems in transportation and logistics.
Conclusion
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Zhen Guo is an emerging researcher with a strong academic background and a growing presence in the international engineering research community. His blend of expertise in machine learning and mechanical systems positions him as a promising scholar dedicated to advancing intelligent diagnostics and automation. His contributions as a reviewer and researcher underscore his commitment to innovation and academic excellence.
Publications
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Few-shot sample multi-class incremental fault diagnosis for gearbox based on convolutional-attention fusion network
Authors: Guo, Z.; Du, W.; Liu, Z.; Hu, T.; Yu, Y.; Li, C.
Journal: Expert Systems with Applications -
Squeeze-and-excitation attention residual learning of propulsion fault features for diagnosing autonomous underwater vehicles
Authors: Du, W.; Yu, X.; Guo, Z.; Wang, H.; Pu, Z.; Li, C.
Journal: Journal of Field Robotics -
Unsupervised anomaly detection for gearboxes based on the deep convolutional support generative adversarial network
Authors: Chengguang Zhang; Zhen Guo; Chuan Li
Journal: Scientific Reports -
Channel attention residual transfer learning with LLM fine-tuning for few-shot fault diagnosis in autonomous underwater vehicle propellers
Authors: Wenliao Du; Xinlong Yu; Zhen Guo; Hongchao Wang; Yiyuan Gao; Ziqiang Pu; Guanghua Li; Chuan Li
Journal: Ocean Engineering -
Fault diagnosis of rotating machinery with high-dimensional imbalance samples based on wavelet random forest
Authors: Zhen Guo; Wenliao Du; Chuan Li; Xibin Guo; Zhiping Liu
Journal: Measurement
Education
Experience
Awards and Recognition
Skills and Certifications
Research Focus
Conclusion
Publications