I'm Chao Zhao, I am an Associate Professor at the School of Mechanical Engineering and Automation, Northeastern University, China. I earned my Ph.D. in March 2025 from Huazhong University of Science and Technology, under the supervision of Prof. Weiming Shen. During my Ph.D., I was also a visiting scholar at Politecnico di Milano, Italy, under the guidance of Prof. Enrico Zio.
大家好，我是赵超，目前在东北大学（中国沈阳）机械工程与自动化学院任副教授。
欢迎对工业人工智能和智能制造感兴趣的同学加入我的课题组！
📌 课题组现招收硕士研究生（2026年9月入学）
📌 欢迎高年级本科生提前参与科研实践
本课题组注重平等交流、沟通融治，并与以下单位保持紧密合作：
- 华中科技大学 机械学院
- 米兰理工大学 能源系
- 福耀科技大学 等国内外高校
我们将为学生提供联合培养、科研合作与企业交流的机会。
我的论文发表：
Feel free to reach out to me via email: zhaochao0612@gmail.com
If you're interested in any of the following areas, feel free to reach out via email — I’m always open to potential collaborations:
-
Data-Model Hybrid Driven Intelligent Monitoring & Maintenance for Advanced Equipment （数模混合驱动的高端装备智能监测与运维）
-
Application of Foundation Models in Smart Manufacturing （ 工业大模型在智能制造中的应用）
-
Industrial Big Data Governance and Mining （ 工业大数据治理与挖掘）
Here are my repositories related to my review paper:
DG-PHM : This is a repository about Domain Generalization for PHM, including papers, code, datasets etc（基于领域泛化的故障预测与健康管理）.
DGFD-Benchmark : This is a benchmark for domain generalization-based fault diagnosis（基于领域泛化的故障诊断基准实验）.
LLM-PHM : This repository includes paper about LLM-based fault diagnosis and prognosis（基于大模型的故障预测与健康管理）.
Multimodal-PHM : This repository contains papers, code, and datasets related to multi-modal-based fault diagnosis（基于多模态数据的智能故障诊断）.
Some of my repositories related to research paper:
MUGTN : Multimodal unified generalization and translation network for intelligent fault diagnosis under dynamic environments（多模态领域泛化故障诊断）.
AOSDGN : Adaptive open set domain generalization network for unknown faults（开集领域泛化故障诊断）.
SDAGN : Imbalanced domain generalization via Semantic-Discriminative augmentation（不平衡领域泛化故障诊断）.
BWAN : Partial transfer fault diagnosis network（部分领域适应故障诊断）.
MSDGN : Mutual-assistance semi-supervised domain generalization network（半监督领域泛化故障诊断）.
FDDG : Federated distillation domain generalization framework for machinery fault diagnosis（联邦领域泛化故障诊断）.
FedDGMC : Federated Domain Generalization: Secure & Robust Framework for Intelligent Fault Diagnosis（联邦领域泛化故障诊断）.
DAN : Dual adversarial network for cross-domain open set fault diagnosis（开集域适应故障诊断）.
DGNIS : Domain generalization network combining invariance and specificity for real-time fault diagnosis（领域泛化故障诊断）.
Check out some of HUST datasets other datasets:
HUST-bearing-dataset : Bearing failure dataset for intelligent fault diagnosis research（轴承数据集）.
HUST-gearbox-dataset : Gearbox failure dataset for intelligent fault diagnosis research（齿轮数据集）.
HUST-motor-multimodal-dataset : Motor failure dataset with vibration & audio signals（多模态电机数据集）.
HUST-Transmission-system-dataset : Transmission system failure dataset（传动系统数据集）.
Open-source datasets : Open-source mechanical failure dataset with 30+ categories（其他开源数据集）.
Here are some of the repositories that are related to personal summary of research:
The-Ph.D.-journey-scenery : Collected a number of doctoral problems encountered and related information（博士期间摘录的他人的干货）.
