Doctoral student | Wuhan University of Technology | China
Dr. Zhen Guo is an emerging researcher in intelligent fault diagnosis and industrial artificial intelligence, with work spanning advanced data-driven methods for complex mechanical and robotic systems. The academic profile reflects strong training in engineering research with a focus on applying deep learning and statistical learning to real-world reliability challenges. Professional experience includes active service as a peer reviewer for leading international journals and conferences in engineering informatics, artificial intelligence, measurement science, and mechanical systems, demonstrating recognition within the scholarly community. Research interests center on deep learning–based fault diagnosis, rotating machinery health monitoring, robotics, anomaly detection, imbalance learning, few-shot and incremental learning, and transfer learning, particularly for wind turbines, gearboxes, and autonomous systems. Research skills encompass convolutional and attention-based neural networks, adversarial learning, feature extraction, time-series analysis, imbalance data handling, and intelligent condition monitoring frameworks. Contributions are evidenced by publications in high-impact journals such as Renewable Energy, IEEE Transactions on Instrumentation and Measurement, Expert Systems with Applications, Measurement, Ocean Engineering, and Scientific Reports, addressing both theoretical modeling and practical deployment. Awards and honors are reflected through consistent publication in top-tier venues and trusted reviewer roles. Overall, the work demonstrates a coherent trajectory toward robust, scalable, and intelligent diagnostic solutions for next-generation industrial and transportation systems. He has achieved 152 Citations 11 Documents 7h-index.
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