Dr. Zhen Guo | Renewable Energy | Editorial Board Member

Dr. Zhen Guo | Wuhan University of Technology | China

Dr. Zhen Guo is an emerging researcher specializing in intelligent fault diagnosis, deep learning, anomaly detection, and engineering signal processing, with emphasis on developing reliable data-driven methods for modern industrial systems. His work addresses key challenges in rotating machinery, gearboxes, propellers, and wind turbines by integrating advanced machine learning models with domain-specific signal processing techniques to enhance diagnostic accuracy under real-world constraints. Dr. Guo’s research explores multi-scale wavelet decomposition, adaptive feature fusion, and high-dimensional sampling strategies to mitigate multi-level class imbalance and improve the robustness of condition-monitoring frameworks. He has introduced innovative unsupervised anomaly detection approaches using deep convolutional support generative adversarial networks, enabling effective detection in scenarios where labeled data are insufficient or costly. His contributions also include transfer-learning architectures with channel-attention residual networks and LLM-assisted fine-tuning to achieve few-shot fault recognition in complex systems such as autonomous underwater vehicle propellers. Additionally, he has advanced wavelet-random-forest methods for modeling high-dimensional imbalance samples and proposed unified pattern-fusion strategies for mining alarm data in large-scale industrial facilities using adaptive discretization and time-clustering mechanisms. Dr. Guo’s work published in leading journals such as Mechanical Systems and Signal Processing, Scientific Reports, Ocean Engineering, Measurement, and Measurement Science and Technology demonstrates his commitment to bridging the gap between theoretical advances and practical reliability engineering. Supported by competitive research funding, he continues to design predictive frameworks that improve equipment health assessment, Remaining Useful Life estimation, and maintenance decision-making across industrial environments, contributing significantly to the advancement of intelligent diagnostic technologies.

Profile: Scopus | ORCID

Featured Publications

Guo, Z. (2025). Multi-scale wavelet decomposition and feature fusion for rotating machinery fault diagnosis under multi-level class imbalance. Mechanical Systems and Signal Processing.

Guo, Z. (2025). Unsupervised anomaly detection for gearboxes based on the deep convolutional support generative adversarial network. Scientific Reports, (Published July 1, 2025).

Guo, Z. (2025). Channel attention residual transfer learning with LLM fine-tuning for few-shot fault diagnosis in autonomous underwater vehicle propellers. Ocean Engineering.

Guo, Z. (2025). Fault diagnosis of rotating machinery with high-dimensional imbalance samples based on wavelet random forest. Measurement.

Guo, Z. (2025). Alarm data mining in complex industrial facilities using adaptive discretization based on time clustering and unified pattern fusion mining. Measurement Science and Technology, (Published January 31, 2025).