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Profile
个人简介:
潘彤阳,中南大学交通运输工程学院讲师,美国 IEEE 协会会员,湖南省机械故障诊断与测控技术学会理事,世界交通运输大会安全监测与故障诊断技术委员会青年委员。2017 年获西安交通大学工学学士学位,2022 年获西安交通大学工学博士学位,2021-2022 年于新加坡国立大学工业系统工程与管理系访学。长期从事轨道交通装备建模与动态信号处理、大数据智能故障诊断与寿命预测、机械状态健康监测与智能运维等方面的研究工作。主持高校联合创新基金、湖南省自然科学基金等课题 10 项,研发的智能诊断系统在高速列车、火箭发动机、海上风电机群等重大装备广泛应用,在 IEEE Transactions on Industrial Informatics、Mechanical System and Signal Processing 等国际著名学术期刊发表研究论文 40 余篇(含 ESI 热点论文 1 篇、高被引论文 3 篇),获中国铁道学会科学技术一等奖 1 项。
招生计划:
每年招收硕士研究生3名,课题组氛围愉快、成果丰富,欢迎对深度学习、信号处理、故障诊断、智能算法等方向感兴趣的同学加入课题组攻读学位!
欢迎本科生提前入组交流学习,已指导本科生发表多篇学术论文及最佳会议论文,团队多次竞赛获奖,所在团队多人次获国家奖学金、校长奖学金等奖励荣誉!
联系方式:ty.pan@csu.edu.cn
主持项目:
1. 西安航天动力研究所:全数字试验孪生分析系统
2. 西安航天动力研究所:基于生成对抗网络的复杂装备故障诊断技术
3. 中国运载火箭技术研究院:指控系统智能模型优化设计与验证评估技术
4. 中国航天科技创新研究院:智能算法多层级功能性能评估方法
5. 中国航天科技集团公司第一研究院:基于SCDKWL的ZB故障诊断算法研究
6. 中国航天科技集团公司第六研究院:系统数字孪生与全域动力学性能评估方法研究
7. 湖南省科技厅:先验结构信息约束下瞬变工况运行涡轮泵轴承剩余寿命高可信区间预测
8. 海上风力发电装备与风能高效利用全国重点实验室:复杂海洋环境下全风况运行风电机群关键部件剩余寿命智能预测技术研究
9. 中车四方:逸群快轨车辆修程修制研究及应用分析
10. 中央高校基本科研项目:面向气路故障的多源数据驱动航空发动机剩余寿命智能预测方法研究
典型学术成果:
[1] Integrating Misidentification and OOD Detection for Reliable Fault Diagnosis of High-Speed Train Bogie. IEEE Transactions on Intelligent Transportation Systems
[2] A global and joint knowledge distillation method with gradient-modulated dynamic parameter adaption for EMU bogie bearing fault diagnosis.Measurement
[3] Generative artificial intelligence and data augmentation for prognostic and health management: Taxonomy, progress, and prospects.Expert Systems with Applications
[4] Contrastive feature-based learning-guided elevated deep reinforcement learning: Developing an imbalanced fault quantitative diagnosis under variable working conditions.Mechanical Systems and Signal Processing
[5] A meta-weighted network equipped with uncertainty estimations for remaining useful life prediction of turbopump bearings.Expert Systems with Applications
[6] A multi-head attention network with adaptive meta-transfer learning for RUL prediction of rocket engines.Reliability Engineering & System Safety
[7] A meta network pruning framework for remaining useful life prediction of rocket engine bearings with temporal distribution discrepancy. Mechanical Systems and Signal Processing
[8] Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives.ISA transactions(ESI Highly Cited)
[9] Deep Feature Generating Network: A New Method for Intelligent Fault Detection of Mechanical Systems under Class Imbalance. IEEE Trans. Ind. Informat.
[10] Intelligent fault identification for industrial automation system via multi-scale convolutional generative adversarial network with partially labeled samples. ISA Trans.
[11] A Deep Learning Network via Shunt-wound Restricted Boltzmann Machines Using Raw Data for Fault Detection. IEEE Trans. Instrum. Meas.
[12] A Novel Deep Learning Network via Multi-Scale Inner Product with Locally Connected Feature Extraction for Intelligent Fault Detection. IEEE Trans. Ind. Informat.
[13] Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects. Knowledge-Based Systems