IntroductionFocusing on the intelligence and informatization of mechanical equipment, based on mathematical modeling, model experiments, artificial intelligence, and deep learning, research is conducted on intelligent perception and advanced computing technology of mechanical signals, providing important technical support for intelligent state monitoring, fault diagnosis, life prediction, and intelligent operation and maintenance of mechanical equipment. EducationWork ExperienceSocial PositionSocial ActivitiesResearchIntelligent perception and advanced computing of mechanical signals Intelligent monitoring and fault diagnosis of mechanical health status Non-destructive testing and monitoring of structural damage Equipment remaining life prediction and intelligent operation and maintenance Teaching1.Theoretical mechanics, target audience: Mechanical majors, sophomore year 2.Artificial Intelligence and Applications, Teaching Target: Mechanical Engineering Majors, Third Year Students 3.Introduction to Artificial Intelligence and Mechanical Engineering, target audience: Mechanical majors, master's and doctoral students PostgraduatesFundingNational Natural Science Foundation of China General Project, Dynamic Blind Separation of Vibration Signals and Performance Degradation Trend Prediction of Variable Speed Main Bearings with Multiple Fault Coupling, 580000 yuan, January 2021-December 2024 Vertical ProjectHorizontal ProjectPublications[1] Chen Guangyi, Tang Gang*, Zhu Zhixiao. VKCNN: An interpretable variational kernel convolutional neural network for rolling bearing fault diagnosis. Advanced Engineering Informatics, 62, 102705, 2024. [2] Binghuan Cai, Gang Tang*. Maximum spectral sparse entropy blind deconvolution for bearing fault diagnosis. IEEE Sensors Journal, 24(5):6451-6468, 2024. [3] Zhixiao Zhu, Guangyi Chen, Gang Tang*. Domain Adaptation With Multi-Adversarial Learning for Open-Set Cross-Domain Intelligent Bearing Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement，72, 3533411,2023. [4] Dongdong Wang, Yining Dong*, Han Wang, Gang Tang*. Limited fault data augmentation with compressed sensing for bearing fault diagnosis. IEEE Sensors Journal, 23(13):14499-14511, 2023. [5] Xianling Li, Hao Hu, Shichao Zhang, Gang Tang*. A Fault Diagnosis Method for Rotating Machinery with Semi-supervised Graph Convolutional Network and Images Converted from Vibration Signals. IEEE Sensors Journal, 23(11):11946-11955, 2023. (非第一单位) [6] Zhou Yuxuan, Dong Yining, Tang Gang. Time-varying Online Transfer Learning for Intelligent Bearing Fault Diagnosis with Incomplete Unlabeled Target Data. IEEE Transactions on Industrial Informatics, 19(6):7733-7741.2023. [7] Cai Binghuan, Zhang Long, Tang Gang*. Encogram: An autonomous weak transient fault enhancement strategy and its application in bearing fault diagnosis. Measurement, 206, 112333, 2023. [8] Gang Tang*, Hao Hu, Jian Kong, Haoxiang Liu. A Novel Fault Feature Selection and Diagnosis Method for Rotating Machinery with Symmetrized Dot Pattern Representation. IEEE Sensors Journal, 23(2):1447-1461, 2023. Awards2021/2023 Outstanding Reviewers Award，IEEE Instrumentation and Measurement Society PatentHonor RewardAdmissions Information |