刘征 博士、教授、硕士生导师
研究领域：风电装备可靠性、可靠性数字孪生、智能运维
办公地点：创A 501
办公邮箱：liuzheng@gzhu.edu.cn
个人简介：
刘征，研究方向主要包括风电装备可靠性、可靠性数字孪生、智能运维。目前共主持国家自然科学基金2项、广东省自然科学基金2项、广州市科技发展计划1项，参与国家重点研发计划1项、国家科技重大专项课题4项、国家自然科学基金2项，其他项目2项；共发表论文40余篇，研究成果发表在Reliability Engineering and System Safety, IEEE Transactions on Reliability, Composite Structures, Engineering Failure Analysis, Ocean Engineering, Quality and Reliability Engineering International等国际期刊上；为Reliability Engineering and System Safety、Engineering Failure Analysis等国际期刊审稿人。
教育背景:
◆2010-2016 电子科技大学 机械工程 博士
◆2006-2010 东北大学 工业设计 学士
职业经历:
◆ 2025.08-至今 广州大学 机械与电气工程学院 教授
◆ 2020.01-2025.07 广州大学 机械与电气工程学院 副教授
◆ 2016.08-2019.12 广州大学 机械与电气工程学院 讲师
访学经历:
◆ 2024.09-2025.08 剑桥大学 工程学院制造系 访问学者
◆ 2022.01-2022.07 华东理工大学 机械与动力工程学院 访问学者
教授课程:
《机械设计》《机械可靠性设计》《机械创新与发明》等
科研项目:
◆ 国家自然科学基金面上项目，多环境应力耦合作用下考虑尺寸效应的海上风机叶片后缘疲劳可靠性研究，2022/01-2025/12，主持；
◆ 国家自然科学基金青年基金，耦合封装-粘结-连接疲劳特性的叶片测试FBG传感器可靠性评价，2020.03-2022.12，主持；
◆ 国家重点研发计划《大型海上风电机组叶片测试技术研究及测试系统研制》，2019.03-2022.03，项目骨干；
◆ 广东省自然科学基金海上风电联合基金-面上项目，尺寸、形状效应下考虑多初始缺陷的海上风机叶片疲劳可靠性分析与抗疲劳设计，2022.10-2025.10，主持；
◆ 广东省自然科学基金海上风电联合基金-面上项目，低安全裕度下考虑大尺寸、超柔性结构特征的大型海上风机叶片损伤失效分析与疲劳寿命预测，2024.11-2027.10，主持；
◆ 广州市科技计划，不完全监测下的在役风力机叶片疲劳寿命预测与可靠性评估，2019.03-2022.03，主持。
研究成果:
◆ 科研论文
1. Z.J. Shao, Z. Liu*, J.L. Liang, et al (2025). A developed Fick's Law and HSV-based fatigue reliability modeling method for CFRP/Epoxy adhesive structures considering environmental stresses and size effects. Reliability Engineering and System Safety, 264: 111336. https://doi.org/10.1016/ j.ress.2025.111336.
2. Z. Liu, Z.J. Shao, Y.J. Li, et al (2025). Performance degradation analysis-based fatigue reliability modeling and assessment for offshore wind turbine blade adhesive bonding. IEEE Transactions on Reliability. https://doi.org/10.1109/TR.2025.3563999.
3. H.D. Liu, Z. Liu*, L. Tu, et al (2025). Fatigue Life Prediction and Reliability Assessment of Cracked CFRP Laminates in WTBs Application: A Combined Approach Using Paris-XFEM and ALK Techniques. IEEE Transactions on Reliability. https://doi.org/10.1109/TR.2025.3560625.
4. Z. Liu, J.L. Liang, Z. He, et al (2025). Finite element submodeling technique-based fatigue analysis and reliability modeling of wind turbine blade trailing edge, Composite Structures, 352: 118699
5. J.L. Liang, Z. Liu*, Z. He, et al (2025). Development of a novel finite element submodeling technique for fatigue reliability modeling and assessment of wind turbine blades, Ocean Engineering, 316: 119934
6. Z.J. Shao, Z. Liu*, Y.H. Zhang, et al (2025). Fatigue Life Prediction and Reliability Assessment of CFRP Adhesively Bonded Joints in Offshore Wind Turbine Blade Applications: A Physics-Informed Data-Driven Approach. Quality and Reliability Engineering International, 41:943–956. https://doi.org/10.1002/qre.3715.
7. Z. Liu, J.L. Liang, Z. He, et al (2024). A developed fatigue analysis approach for composite wind turbine blade adhesive joints using finite-element submodeling technique. Engineering Failure Analysis. 164(108701)
8. Z. Liu, H.D. Liu, Z.J. Shao, et al (2024). Research on fatigue reliability of wind turbine blade adhesive bonding considering parameter uncertainty. Quality and Reliability Engineering International,1-18. DOI: 10.1002/qre.3564.
8. YJ Li, Z Liu*, ZF He, et al. (2023). Fatigue reliability analysis and assessment of offshore wind turbine blade adhesive bonding under the coupling effects of multiple environmental stresses. Reliability Engineering and System Safety, 238:109426. https://doi.org/10.1016/j.ress.2023.109426.
10. Z Liu, ZF He, L Tu, et al. (2022). A fatigue reliability assessment approach for wind turbine blades based on continuous time Bayesian network and FEA, Quality and Reliability Engineering International, 10:1-19. https://doi.org/10.1002/qre.3262
11. Z Liu, YJ Li, N Zhang, et al. (2021). Reliability Analysis of CFRP-Packaged FBG Sensors Using FMEA and FTA Techniques, Applied Sciences, 11: 10859. https://doi.org/10.3390/app112210859.
12. PY Zhu, XB Feng, Z Liu*, et al. (2021). Reliable packaging of optical fiber Bragg grating sensors for carbon fiber composite wind turbine blades, Composites Science and Technology, 213: 108933. https://doi.org/10.1016/j.compscitech.2021.108933.
13. Z Liu, X Liu, S.P. Zhu, et al. (2020). Reliability assessment of measurement accuracy for FBG sensors used in structural tests of the wind turbine blades based on strain transfer laws, Engineering Failure Analysis, 112, 104506. https://doi.org/10.1016/j.engfailanal.2020.104506.
14. Z Liu, X Liu, Kan Wang, et al. (2019), GA-BP Neural Network-based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades, Energies, 12(6), 1-15. https://doi.org/10.3390/en12061026.
15. Z Liu, X Liu, H Huang, et al. (2019). A new inherent reliability modeling and analysis method based on imprecise Dirichlet model for machine tool spindle. Annals of Operations Research, 1-16. https://doi.org/10.1007/s10479-019-03333-9.
16. X Liu, Z Liu*, Z Liang, et al. (2019). PSO-BP Neural Network-based strain prediction of wind turbine blades. Materials, 2019, 12:1-15. https://doi.org/10.3390/ma12121889.
17. Z Liu and X Liu (2018). An Improved Approach by Introducing Copula Functions for Structural Reliability Analysis under Hybrid Uncertainties, Advances in Mechanical Engineering, 10(7), 1-10. https://doi.org/10.1177/1687814018785839.
18. Z Liu and X Liu (2018). Storage reliability assessment for the stored equipment under periodical inspection, Advances in Mechanical Engineering, 10(6), 1-7. https://doi.org/10.1177/1687814018782312.
19. Z Liu, L Yu, Y Li, et al. (2018). Comparisons of Two Non-probabilistic Structural Reliability Analysis Methods for Aero-engine Turbine Disk, Journal of Turbo and Jet Engines, 34(2), 295-303. https://doi.org/10.1515/tjj-2016-0010.
◆ 专利申请
[1]刘征，李永杰，何振锋，等. 一种多环境应力影响下的复合材料胶接件疲劳分析方法(CN115683897A).
[2]刘征，李永杰，梁金龙，等. 一种纤维增强复合材料层合板制备成型装置(CN202210719270.2).
[3]刘征，涂亮，陈颖婷，等. 玻纤胶接接头浸泡式盐水老化-拉伸剪切复合试验装置(CN202210716922)
[4]刘征，涂亮，李嘉晴，等. 一种风机叶片玻纤层合板盐雾-弯曲疲劳复合试验装置(CN202210718499.4).
[5]刘征，何振锋，简昌煌，等. 一种片状试样拉伸扭转试验机(CN202210668300.1).
[6]刘征，何振锋，蔡梓豪，等. 一种多角度试样拉伸剪切试验机(CN202210669600.1)
