 | 特任研究员 |
| 电子邮箱：leimao82@ustc.edu.cn | |
| 电话：0551-63607985 | |
| 地址：力二楼401 | |
| 精密机械与精密仪器系 | |
| 中国科学技术大学西区 | |
| 安徽省合肥市黄山路443号 |
教育经历：
2012.11 英国爱丁堡大学 基础设施与环境 博士
2007.07 中国科学技术大学 机械电子工程 硕士
2004.07 合肥工业大学 交通工程 学士
工作经历：
2018.05-至今 中国科学技术大学精密机械与精密仪器系 特任研究员
2013.11-2018.03 英国拉夫堡大学 航天与汽车工程系 助理研究员
2012.01-2013.10 英国朴次茅斯大学 土木工程与测量系 助理研究员
承担项目：
中国科学技术大学人才引进启动经费，2017-2020
主要讲授课程：
研究生课程 《机电控制系统分析与设计》
研究兴趣：
社会职务：
国际工程师协会（International Association of Engineers）会员
国际预测与健康管理组织（Prognostics and Health Management Society）会员
中国振动工程协会故障诊断专业委员会理事
近期论文：
1. Mao, L.*, Jackson, L. (2018). Effect of sensor set size on polymer electrolyte membrane fuel cell fault diagnosis, Sensors, 18(9):2777.
2. 毛磊 (2018).基于贝叶斯网络的燃料电池故障诊断.振动与冲击, 37:282:287
3. Mao, L.*, Jackson, L., Davies, B. (2018). Effectiveness of a novel sensor selection algorithm in PEM fuel cell on-line diagnosis. IEEE Transactions on Industrial Electronics, 65(9), 7301-7310.
4. Mao, L. *, Jackson, L., Davies, B. (2018). Investigation of PEMFC fault diagnosis with consideration of sensor reliability. International Journal of Hydrogen Energy. 43(35):16941-16948.
5. Mao, L. *, Davies, B., Jackson, L. (2017). Application of sensor selection approach in polymer electrolyte membrane fuel cell prognostics and health management. Energies， 10(10), 1511.
6. Hou, C., Mao, L., Lu, Y.* (2017). Experimental study of extracting artificial boundary condition frequencies for dynamic model updating. Smart Structures and Systems, 20(2): 247-261.
7. Mao, L. *, Jackson, L., Jackson, T. (2017). Investigation of polymer electrolyte membrane fuel cell internal behaviour during long term operation and its use in prognostics. Journal of Power Sources, 362:39-49.
8. Mao, L. *, Lu, Y. (2017). Experimental study of sensitivity-aided application of artificial boundary condition frequencies for damage identification. Engineering Structures, 134:253-261.
9. Mao, L. *, Barnett, S.J. (2017). Investigation of toughness of ultra-high performance fibre reinforced concrete (UHPFRC) beam under impact loading. International Journal of Impact Engineering, 99:26-38.
10. Mao, L. *, Jackson, L., Dunnett, S.J. (2017). Fault diagnosis of practical polymer electrolyte membrane (PEM) fuel cell system with data-driven approaches. Fuel Cells, 17(2): 247-258.
11. Mao, L. *, Jackson, L. (2016). Selection of optimal sensors for predicting performance polymer electrolyte membrane fuel cell. Journal of Power Sources, 328:151-160.
12. Mao, L. *, Jackson, L. (2016). Comparative study on prediction of fuel cell performance using machine learning approaches. Lecture Notes in Engineering and Computer Science, 1:52-57.
13. Mao, L. *, Lu, Y. (2016). Selection of optimal artificial boundary condition (ABC) frequencies for structural damage identification. Journal of Sound and Vibration, 374:245-259.