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个人信息Personal Information
教师英文名称：Tongyang Pan
教师拼音名称：pantongyang
电子邮箱：
入职时间：2023-05-30
所在单位：交通运输工程学院
学历：博士研究生毕业
办公地点：铁道学院交通楼412
性别：男
联系方式：ty.pan@csu.edu.cn
学位：工学博士学位
在职信息：在职
毕业院校：西安交通大学
- Integrating Misidentification and OOD Detection for Reliable Fault Diagnosis of High-Speed Train Bogie.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- A global and joint knowledge distillation method with gradient-modulated dynamic parameter adaption for EMU bogie bearing fault diagnosis.Measurement
- Generative artificial intelligence and data augmentation for prognostic and health management: Taxonomy, progress, and prospects.Expert Systems with Applications
- Contrastive feature-based learning-guided elevated deep reinforcement learning: Developing an imbalanced fault quantitative diagnosis under variable working conditions.MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A meta-weighted network equipped with uncertainty estimations for remaining useful life prediction of turbopump bearings.Expert Systems with Applications
- A multi-head attention network with adaptive meta-transfer learning for RUL prediction of rocket engines.Reliability Engineering & System Safety
- A meta network pruning framework for remaining useful life prediction of rocket engine bearings with temporal distribution discrepancy.Mechanical Systems and Signal Processing
- Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives.ISA transactions(ESI Highly Cited)
- Deep Feature Generating Network: A New Method for Intelligent Fault Detection of Mechanical Systems under Class Imbalance.IEEE Trans. Ind. Informat.
- Intelligent fault identification for industrial automation system via multi-scale convolutional generative adversarial network with partially labeled samples.ISA Trans.
- A Deep Learning Network via Shunt-wound Restricted Boltzmann Machines Using Raw Data for Fault Detection.IEEE Trans. Instrum. Meas.
- A Novel Deep Learning Network via Multi-Scale Inner Product with Locally Connected Feature Extraction for Intelligent Fault Detection.IEEE Trans. Ind. Informat.
- Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects.Knowledge-Based Systems
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