副教授
韩华

  基本信息                                     

  姓   名:韩  华

  职    称:副教授

  通讯地址:上海市杨浦区军工路516

  邮  编:200093

  电  话:13611880360

    Emailhappier_han@126.com


  

个人简介

韩华,女,内蒙古乌兰察布人,工学博士,硕士生导师,长期从事制冷及空调系统相关故障诊断、AIHVAC&R系统中的应用、电卡制冷、振荡热管等研究。发表SCI论文30余篇,被引500余次,其中一篇被列为ESI高被引论文,一篇入选F5000领跑者计划。曾多次担任国外专家技术讲座现场翻译,担任Applied EnergyInternational Journal of RefrigerationEnergy and BuildingsApplied Thermal Engineering等多个国际期刊的审稿人,在日本横滨举办的国际制冷大会上受邀担任分会场主席。同济大学三八红旗手,上海交通大学优秀毕业生。硕士期间,在国际大学生论文竞赛中进入Top 5,中国学生首次,作为中国学生代表在ISC国际管理研讨会上做1.5小时的主题演讲。


教育及访学经历

2012年博士毕业于上海交通大学 机械与动力工程学院 制冷与低温专业

2001-2002年在香港理工大学 屋宇设备工程系 助理研究员

1999年硕士毕业于同济大学 供热、供燃气、通风及空调工程专业

1996年学士毕业于河北工程大学  供热、通风及空调工程专业


工作经历

2012年起在上海理工大学能源与动力工程学院制冷及低温工程研究所任教,之前曾在新晃空调设备有限公司、上海信宁科技有限公司、三丰医疗科技有限公司等单位从事技术、翻译、设计管理等工作。

  

主要研究方向

制冷空调系统的故障诊断及优化、AI及数字孪生在制冷系统中的应用、电卡制冷等


长期致力于制冷系统故障检测、诊断及预测研究,将人工智能方法与传统的专业知识相结合,提出了重要传感器识别、敏感特征提取、故障早期预测、深度特征挖掘等思想,建立了一系列性能较佳的模型,搭建了智能诊断平台。及时检测并诊断故障,对系统的运行状况进行预测,有助于及时排除故障、调整系统,使其运行在最佳能效状态,更加有效地提供服务的同时节能减排,助力双碳目标的达成。


主要科研项目

国家自然科学基金青年基金项目1项 主持

国家自然科学基金面上项目2项 主要完成人

与企业合作的故障诊断项目2项 主持

 

代表性论著

专  著:

  1. Heat Pipes---Design, Applications and Technology》(参编),NOVA, USA, 2018.

  2. 《直面矛盾》(参编),同济大学出版社,1999.


期刊论文:

  1. Hua Han*; Bo Gu; Ting Wang; Zhengrong Li. Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning, International Journal of Refrigeration, 2011, 34(2): 586-599.(ESI高被引论文)

  2. Hua Han*, Ling Xu, Xiaoyu Cui, Yuqiang Fan. Novel Chiller Fault Diagnosis Using Deep Neural Network (DNN) with Simulated Annealing (SA). International Journal of Refrigeration, 2021, 121: 269-278.

  3. Hua Han, Xiaoyu Cui*, Yue Zhu, Tianxiao Xu, Yuan Sui, Shende Sun. Experimental study on a closed-loop pulsating heat pipe (CLPHP) charged with water-based binary zeotropes and the corresponding pure fluids. Energy, 2016, 109: 724-736.

  4. Hua Han*, Zhan Zhang, Xiaoyu Cui, Qinghong Meng. Ensemble Learning with Member Optimization for Fault Diagnosis of a Building Energy System. Energy and Buildings, 2020, 226: 110351.

  5. Hua Han*, Xiaoyu Cui, Yuqiang Fan, Hong Qing. Least squares support vector machine (LS-SVM)-based chiller fault diagnosis using fault indicative features. Applied Thermal Engineering, 2019, 154:540-547.

  6. Hua Han, Xiaoyu Cui*, Yue Zhu, Shende Sun. A comparative study of the behavior of working fluids and their properties on the performance of pulsating heat pipes (PHP). International Journal of Thermal Sciences, 2014, 82: 138-147.

  7. Hua Han*, Zhikun Cao, Bo Gu, Neng Ren. PCA-SVM-Based Automated Fault Detection and Diagnosis (AFDD) for Vapor-Compression Refrigeration Systems. International Journal of HVAC&R Research. 2010, 16(3): 295-313.

  8. H. Han*, B. Gu, J. Kang, Z.R. Li. Study on a Hybrid SVM Model for Chiller FDD Applications, Applied Thermal Engineering. 2011, 31: 582-592.

  9. Hua Han*, Bo Gu, Yingchun Hong, Jia Kan. Automated FDD of multiple- simultaneous faults (MSF) and the application to building chillers, Energy and Buildings. 2011, 43: 2524–2532.

  10. H. Han, S.M. Deng*. A Study on Residential Clothes Drying Using Waste Heat Rejected from a Split-Type Room Air Conditioner (RAC). Drying Technology. 2003, 21(8): 1471- 1490.

  11. Y. Gao, H. Han*, Z.X. Ren, J.Q. Gao, S.X. Jiang, Y.T Yang. Comprehensive study on sensitive parameters for chiller fault diagnosis. Energy and Buildings. 2021, 251: 111318.

  12. Zhan Zhang, Hua Han*, Xiaoyu Cui, Yuqiang Fan.Novel application of multi-model ensemble learning for fault diagnosis in refrigeration systems [J]. Applied Thermal Engineering, 2020, 164(1): 1-11.

  13. Jiaqing Gao, Hua Han*, Zhengxiong Ren, Yuqiang Fan. Fault diagnosis for building chillers based on data self-production and deep convolutional neural network[J]. Journal of Building Engineering, 2021, 34: 102043.

  14. Zhengxiong Ren, Hua Han*, Xiaoyu Cui, Hong Qing, Huiyun Ye. Application of PSO-LSSVM and hybrid programming to fault diagnosis of refrigeration systems[J]. Science and Technology for the Built Environment, 2020: 1-21.

  15. Qingqing Liang, Hua Han, Xiaoyu Cui*, et al. Comparative study of probabilistic neural network and back propagation network for fault diagnosis of refrigeration systems [J]. Science and Technology for the Built Environment, 2018,24: 448-457.

  16. Yuqiang Fan, Xiaoyu Cui*, Hua Han*, Hailong Lu. Feasibility and Improvement of Fault Detection and Diagnosis Based on Factory-Installed Sensors for Chillers [J]. Applied Thermal Engineering, 2020, 164(1):1-10.

  17. Yuqiang Fan, Xiaoyu Cui*, Hua Han*,Hailong Lu. Chiller fault diagnosis with field sensors using the technology of imbalanced data [J]. Applied Thermal Engineering, 2019, 159(8): 1-12.

  18. Yuqiang Fan, Xiaoyu Cui*, Hua Han*, Hailong Lu. Chiller Fault Detection and Diagnosis by Knowledge Transfer Method using Adaptive Imbalanced Processing Technology [J], Science and Technology for the Built Environment, 2020, 26(8):1-23.

  19. Yue Zhu, Xiaoyu Cui*, Hua Han, Shende Sun. The study on the difference of the start-up and heat-transfer performance of the pulsating heat pipe with water-acetone mixtures [J].International Journal of Heat and Mass Transfer, 2014, 77: 834–842.

  20. Saiyan Shi, Xiaoyu Cui*, Hua Han, Jianhua Weng, Zhihua Li. A study of the heat transfer performance of a pulsating heat pipe with ethanol-based mixtures [J].Applied Thermal Engineering, 2016, 102: 1219–1227.

  21. 梁晴晴, 韩华, 崔晓钰*, 谷波. 基于主元分析-概率神经网络的制冷系统故障诊断[J]. 化工学报. 2016(67): 1022-1031.

  22. 范雨强, 崔晓钰, 韩华*, 陆海龙. 不平衡数据技术在冷水机组故障诊断中的应用[J]. 工程热物理学报, 2019(6):1219-1228.

  23. 武浩, 韩华*, 崔晓钰, 范雨强, 徐玲. 基于概念漂移检测的制冷系统故障诊断模型自适应[J]. 制冷学报, 2019(4):121-128.

  24. 徐玲, 韩华*, 崔晓钰, 范雨强, 武浩. 基于PSO优化BP的冷水机组故障诊断研究[J]. 制冷学报, 2019(3):115-123+131.


会议论文:

  1. Yuqiang Fan, Hua Han*, Xiaoyu Cui*, Hao Wu, Ling Xu, Hailong Lu, Hui Geng. Chiller fault diagnosis with the technology of imbalanced data[C]. The 25th IIR International Congress of Refrigeration, Montreal, Canada, 2019.

  2. Qingqing Liang, Hua Han, Xiaoyu Cui*, Hang Ren. Fault detection and diagnosis of a refrigeration system using probabilistic neural network. The 24th International Congress of Refrigeration [ICR2015], Hengbin, Japan, 2015.

  3. H. Han, B. Gu*, T. Wang, Z.R. Li. Study on fault indicative features in the automated fault detection and diagnosis (FDD) for chillers.The 7th International Symposium on Heating, Ventilating and Air Conditioning (ISHVAC2011), Shanghai, Nov. 6-9, 2011, Volume IV: 1281-1290.

  4. Hua Han, Wen-Hua Xu*, Cun-Yang Fan. Particle Contamination Control of Comfort Air Conditioning System.ACHRB: air conditioning in high rise buildings '2000 (Shanghai, 24-27 October 2000), ID: B008.(首次提出“空调系统临界过滤效率”概念)

  5. Han Hua. What’s In Her Way? The 28th International Management Symposium, St Gallen, Switserland, 1998/5.(top 5/250 across the world)

  6. 任正雄,韩华*,金子玥,高嘉檠,高雨,张展. 基于分歧的制冷系统半监督故障诊断. 中国工程热物理学会工程热力学与能源利用学术会议. 上海, 2020,文章编号: 201383.

  7. 高嘉檠,韩华*,任正雄,高雨,江松轩. 基于CNN-ECOC离心式冷水机组故障诊断研究. 中国工程热物理学会工程热力学与能源利用学术会议. 上海, 2020, 文章编号: 201123.

  8. 高雨,韩华*,任正雄,高嘉檠,江松轩. 基于敏感性分析的随机森林模型在冷水机组故障诊断中的应用. 中国工程热物理学会工程热力学与能源利用学术会议. 上海, 2020, 文章编号: 201044.

  9. 徐玲,韩华*,崔晓钰,范雨强,武浩,张展. 基于DNN离心式冷水机组故障诊断研究. 中国工程热物理学会工程热力学与能源利用学术会议. 大连,2018, 文章编号: 181115.