金晓航

研究领域： 机械工程，机械电子工程，人工智能

研究兴趣： 机电系统状态监测、故障诊断与预测、智能维护与控制；大数据分析、新一代人工智能技术及其应用；信号检测与处理。

个人简介： 金晓航，香港城市大学博士、美国内布拉斯加大学-林肯分校博士后、浙江工业大学副教授、博士生导师。

IEEE高级会员、中国仪器仪表学会高级会员、中国振动工程学会高级会员、中国振动工程学会机械动力学专业委员会常务委员/副秘书长、中国振动工程学会故障诊断专业委员会理事、浙江省振动工程学会理事、浙江省工程图学学会理事/副秘书长、IEEE PHM/NCAA国际会议技术委员会委员；《振动工程学报》青年编委。

主要从事机电装备的信号分析、可靠性评估、健康状态监测、故障诊断与预测、智能维护与控制，新一代人工智能技术及其应用、工业大数据分析等方面的研究工作。

主持国家重点研发计划、国家自然科学基金，浙江省自然科学基金，宁波市自然科学基金，国家重点实验室等科研项目10多项。

参与国家自然科学基金、美国自然科学基金，美国能源部、香港研资局、香港创新科技署等单位资助的研究项目10多项。

已发表国内外学术论文80余篇，其中ESI高被引论文3篇，单篇论文被引用次数超400次，IEEE会刊期刊论文28篇。授权发明专利6项。担任IEEE Transactions on Industrial Electronics等20多个ESI期刊审稿专家。

代表性论文

1. Xiaohang Jin, Zhuangwei Xu, and Wei Qiao, “Condition monitoring of wind turbine generators using SCADA data analysis,” IEEE Transactions on Sustainable Energy, vol. 12, no. 1, pp. 202-210, Jan. 2021. [ESI高被引论文]

2. Xiaohang Jin, Jicong Fan, and Tommy W. S. Chow, “Fault detection for rolling-element bearings using multivariate statistical process control methods,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 9, pp. 3128-3136, Sept. 2019.

3. Xiaohang Jin, Zijun Que, Yi Sun, Yuanjing Guo, and Wei Qiao, “A data-driven approach for bearing fault prognostics,” IEEE Transactions on Industry Applications, vol.55, no. 4, pp.3394-3401, July/August 2019.

4. Xiaohang Jin, Wei Qiao, Yayu Peng, Fangzhou Cheng, and Liyan Qu, “Quantitative evaluation of wind turbine faults under variable operational conditions,” IEEE Transactions on Industry Applications, vol. 52, no. 3, pp.2061-2069, May/June. 2016.

5. Xiaohang Jin, Mingbo Zhao, Tommy W. S. Chow, and Michael Pecht, “Motor bearing fault diagnosis using trace ratio linear discriminant analysis,” IEEE Transactions on Industrial Electronics, vol. 61, no. 5, pp. 2441-2451, May 2014. [ESI高被引论文]

6. Yu Wang, Yizhen Peng, Yanyang Zi, Xiaohang Jin, and Kwok-Leung Tsui, “A two-stage data-driven based prognostic approach for bearing degradation problem,” IEEE Transactions on Industrial Informatics, vol. 12, no.3, pp. 924-932, June 2016. [ESI高被引论文]

授权发明专利

1. 金晓航等, 一种基于二元混合过程的轴承剩余寿命预测方法, ZL201911210732.2

2. 金晓航等, 一种基于二元维纳过程的轴承剩余寿命预测方法, ZL201810093716.9

3. 金晓航等, 一种基于扩展卡尔曼滤波算法的轴承故障诊断及预测方法 ZL201510482848.7

4. 金晓航等, 一种基于特征选取和马氏距离的电机健康监测和异常诊断方法 ZL201410216589.9

指导学生获得的项目

1. “基于工业大数据的复杂机电装备故障预测与健康管理研究”获“2020年浙江省大学生科技创新活动计划暨新苗人才计划”资助

2. “基于生成式对抗网络和迁移学习的风电机组状态监测研究”获“2021年浙江省教育厅一般科研项目”资助

3. “数据驱动的风电机组故障诊断与剩余寿命预测”获“2022年浙江省大学生科技创新活动计划暨新苗人才计划”资助

奖励荣誉

20. 2024-2025（1）学期《工程图学》本科教学“优课优酬”奖励（2024年11月）

19. 2023-2024（2）学期《可靠性工程》本科教学“优课优酬”奖励（2024年10月）

18. 连续四年入选“2024年全球前2%科学家榜单”（World's Top 2% Scientists）（2024年9月）

17. 2023-2024（2）学期《智能运维与健康管理》教学示范课（2024年4月）

16.  浙江工业大学机械工程学院2023年度公共服务工作突出奖（2024年1月）

15.  2023年度《计算机集成制造系统》优秀审稿专家（2024年1月）

14.  2022-2023（2）学期《智能运维与健康管理》《可靠性工程》《工程图学》本科教学“优课优酬”奖励（2023年11月）

13. 入选“2023年全球前2%科学家榜单”（World's Top 2% Scientists）（2023年10月）

12. 浙江工业大学“十四五”高层次人才培育计划C类人才培育对象（2023年6月）

11. 2022年度浙江工业大学优秀教师（2023年4月）

10. 浙江工业大学机械工程学院首届青年教师科技成果展优秀作品奖（2023年2月）

09. 2022年度浙江工业大学机械工程学院优秀教师、先进工作者

08. 2022年度《计算机集成制造系统》优秀审稿专家

07. 2021年度《仪器仪表学报》杰出评审专家

06. 2020年度《计算机集成制造系统》优秀审稿专家

05. 2020年度《仪器仪表学报》杰出评审专家

04. 2018年度IEEE Transactions on Instrumentation and Measurement优秀审稿专家

03. 香港城市大学杰出学术表现奖（Outstanding Academic Performance Award）2012、2013

02. 上海交通大学优秀毕业生

01. 浙江省普通高等学校优秀毕业生

联系方式

Email: xhjin@zjut.edu.cn

地址: 浙江省杭州市西湖区留和路288号

邮编: 310023

纵向项目（负责人）

1. 国家重点研发计划，大数据驱动的风电装备智能运维关键技术研究

2. 国家自然科学基金，时空图学习驱动的风电机组可解释智能诊断

3. 国家自然科学基金，基于机电混合数据驱动的风力发电机故障诊断与预测方法研究

4. 浙江省自然科学基金，变工况下机电装备的故障诊断与预测方法研究

5. 宁波市自然科学基金重点项目，工业大数据驱动的大型风电机组状态监测与剩余寿命预测方法研究

6. 宁波市自然科学基金重点项目，基于随机过程建模的风力发电机组故障预测关键问题研究

7. 工业产品环境适应性全国重点实验室开放课题，新能源装备的服役安全与寿命预测技术

8. 工业控制技术国家重点实验室开放课题，基于单分类模型的复杂机电装备运行状态监测研究

9. 工业控制技术国家重点实验室开放课题，基于粒子滤波算法的机电设备性能退化与故障预测研究

10. 石油天然气装备教育部重点实验室开放基金项目，海上风电清洁能源装备的智能诊断

企业委托项目（负责人）

1. 环保设备的机械结构设计与智能控制

2. 灯具的可靠性分析

3. 训练器材的工程设计

4. 物联网模块的合作开发

5. 橡胶材料强度耐热性能退化因素分析研究

6. 退化过程寿命预测算法开发

参与部分科研项目：

1. Office of Energy Efficiency and Renewable Energy (EERE), U.S. Department of Energy, An Online Intelligent Prognostic Health Monitoring System for Wind Turbines (DE-EE0006802)

2. U.S. National Science Foundation, Cognitive Prediction-Enabled Online Intelligent Fault Diagnosis and Prognosis for Wind Energy Systems (ECCS-1308045)

3. Research Grants Council (RGC) Collaborative Research Fund, Self-Cognizant Prognostics for Electronics-Rich System (RGC CRF #CityU8/CRF/09)

Refereed Journal Papers:

45. Gang Xiao, Ying Cao, Jiacheng Huang, Xiaohang Jin, and Yuanming Zhang, “Knowledge graph metric learning network for few-shot health status assessment,” IEEE Sensors Journal, Accepted for publication.

44. Xinguo Qiu, Tong Shen, Fucheng Wang, Xiaohang Jin, and Ying Zheng, “A position-free control strategy in the full speed domain based on an electro-hydraulic pump,” Measurement and Control, Accepted for publication.

43. Yulin Zhang, Ziqian Kong, Ziqi Wang, Xiaohang Jin, and Zhengguo Xu, “Remaining useful life prediction with uncertainty quantification based on multi-distribution fusion structure,” Reliability Engineering & System Safety, vol. 251, November 2024, Art no. 110383.

42. Xiaohang Jin, Shengye Lv, Ziqian Kong, Hongchun Yang, Yuanming Zhang, Yuanjing Guo, and Zhengguo Xu, “Graph spatio-temporal networks for condition monitoring of wind turbine,” IEEE Transactions on Sustainable Energy, vol. 15, no. 4, pp. 2276-2286, October 2024.

41. Zixuan He, Ziqian Kong, Xiaohang Jin, Zijun Que, and Zhengguo Xu, “Remaining useful life prediction of radioisotope thermoelectric generator using a component-level mechanism-based nonlinear additive Wiener process,” IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-14, October 2024, Art no. 3535514.

40. Ziqian Kong, Xiaohang Jin, Feng Wang, and Zhengguo Xu, “Spatio-temporal propagation: An extended message passing graph neural network for remaining useful life prediction,” IEEE Sensors Journal, vol. 24, no. 20, pp. 32468-32479, October 2024.

39. Yixi Wu, Ziqi Wang, Chenli Shi, Xiaohang Jin, and Zhengguo Xu, “A novel data-driven combustion modeling and optimization approach for coal-fired boiler under deep peak shaving,” Energy, vol. 304, September 2024, Art no. 132106.

38. Ziqi Wang, Xiaohang Jin, and Zhengguo Xu, “Incremental learning method for wind turbine fault detection models considering false negatives,” IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-13, September 2024, Art no. 3532013.

37. Jiamu Ling, Chenli Shi, Feng Wang, Wei Ye, Xiaohang Jin, Zhuangwei Xu, and Zhengguo Xu, “Attenuation calibration using priori knowledge guiding multi-objective optimization in distributed fiber sensing,” IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-12, August 2024, Art no. 1006812.

36. Zijun Que, Xiaohang Jin, Zhengguo Xu, and Chang Hu, “Remaining useful life prediction based on incremental learning,” IEEE Transactions on Reliability, vol. 73, no. 2, pp. 876-884, June 2024.

35. Huang Wei, Zhen Liu, Xiaohang Jin, Jinshan Xu, and Xinwei Yao, “Improved autoencoder model with memory module for anomaly detection,” IEEE Sensors Journal, vol. 24, no. 8, pp. 12770-12781, April 2024.

34. Ruchun Zhao, Guoqian Jiang, Qun He, Xiaohang Jin, and Ping Xie, “Current-aided vibration fusion network for fault diagnosis in electromechanical drive system,” IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-10, 2024, Art no. 3510010.

33. Ruxu Yue, Guoqian Jiang, Xiaohang Jin, Qun He, and Ping Xie, “Spatio-temporal feature alignment transfer learning for cross-turbine blade icing detection of wind turbines,” IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-17, 2024, Art no. 3507717.

32. Yuanjing Guo, Shaofei Jiang, Jiangen Fu, Youdong Yang, Yumei Bao, and Xiaohang Jin, “Stabilization temperature prediction in carbon fiber production using empirical mode decomposition and long short-term memory network,” Journal of Cleaner Production, vol. 429, Dec. 2023.

31. Zian Chen, Xiaohang Jin, Ziqian Kong, Feng Wang, and Zhengguo Xu, “Global and local information integrated network for remaining useful life prediction,” Engineering Applications of Artificial Intelligence, vol. 126, Nov. 2023.

30. Yuanming Zhang, Weiyue Zhou, Jiacheng Huang, Xiaohang Jin, and Gang Xiao, “Temporal knowledge graph informer network for remaining useful life prediction,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-10, 2023, Art no. 3528610.

29. Chenli Shi, Ziqi Wang, Xiaohang Jin, Zhengguo Xu, Zhangsheng Wang, and Peng Shen, “A novel three-stage multi-population evolutionary algorithm for constrained multi-objective optimization problems,” Complex & Intelligent Systems, 2023.

28. Ziqi Wang, Xiaohang Jin, and Zhengguo Xu, “An adaptive condition monitoring method of wind turbines based on multivariate state estimation technique and continual learning,” IEEE Transactions on Instrumentation and Measurement, vol. 72, 2023, Art no. 3513009. 

27. Ziqian Kong, Xiaohang Jin, Zhengguo Xu, and Zian Chen, “A contrastive learning framework enhanced by unlabeled samples for remaining useful life prediction,” Reliability Engineering & System Safety, vol. 234, June 2023, Article 109163.

26. Xiaohang Jin, Xiaoying Zhang, Xu Cheng, Guoqian Jiang, Lesedi Masisi, and Wei Huang, “A physics-based and data-driven feature extraction model for blade icing detection of wind turbines,” IEEE Sensors Journal, vol. 23, no. 4, pp. 3944-3954, Feb. 2023.

25. Xiaohang Jin, Hao Wang, Ziqian Kong, Zhengguo Xu, and Wei Qiao, “Condition monitoring of wind turbine based on SCADA data and feature Transfer learning,” IEEE Access, vol. 11, pp. 9441-9450, 2023.

24. Xiaohang Jin, Hengtuo Pan, Chengzuo Ying, Ziqian Kong, Zhengguo Xu, and Bin Zhang, “Condition monitoring of wind turbine generator based on Transfer learning and one-class classifier,” IEEE Sensors Journal, vol. 22, no. 24, pp. 24130-24139, Dec. 2022.

23. Ziqian Kong, Xiaohang Jin, Zhengguo Xu, and Bin Zhang, “Spatio-temporal fusion attention: A novel approach for remaining useful life prediction based on graph network network,” IEEE Transactions on Instrumentation and Measurement, vol. 71, 2022, Art no. 3515912.

22. Yuanjing Guo, Youdong Yang, Shaofei Jiang, Xiaohang Jin, and Yanding Wei, “Rolling bearing fault diagnosis based on successive variational mode decomposition and the EP index,” Sensors, vol. 22, no. 10: 3889, 2022.

21. Yuanjing Guo, Shaofei Jiang, Youdong Yang, Xiaohang Jin, and Yanding Wei, “Gearbox fault diagnosis based on improved variational mode extraction,” Sensors, vol. 22, no. 5: 1779, 2022.

20. Zijun, Que, Xiaohang Jin, and Zhengguo Xu, “Remaining useful life prediction for bearings based on a gate recurrent unit,” IEEE Transactions on Instrumentation and Measurement, vol. 70, 2021, Art no. 3511411.

19. Xiaohang Jin, Zhuangwei Xu, and Wei Qiao, “Condition monitoring of wind turbine generators using SCADA data analysis,” IEEE Transactions on Sustainable Energy, vol. 12, no. 1, pp. 202-210, Jan. 2021. [ESI高被引论文]

18. Xiaohang Jin, Jicong Fan, and Tommy W. S. Chow, “Fault detection for rolling-element bearings using multivariate statistical process control methods,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 9, pp. 3128-3136, Sept. 2019.

17. Xiaohang Jin, Zijun Que, Yi Sun, Yuanjing Guo, and Wei Qiao, “A data-driven approach for bearing fault prognostics,” IEEE Transactions on Industry Applications, vol. 55, no. 4, pp.3394-3401, July/August 2019.

16. Xiaohang Jin, Fangzhou Cheng, Yayu Peng, Wei Qiao, and Liyan Qu, “Drivetrain gearbox fault diagnosis: vibration- and current-based approaches,” IEEE Industry Applications Magazine, vol. 24, no. 6, pp. 56-66, Dec. 2018.

15. Xiaohang Jin, Yu Wang, Tommy W. S. Chow, and Yi Sun, “MD-based approaches for system health monitoring: a review,” IET Science, Measurement&Technology, vol. 11, no. 4, pp. 371-379, July 2017.

14. Yi Sun, Man Liang, Xiaohang Jin, Pengpeng Ji, Jihong Shan, “Experimental and modeling study of the regular polygon angle-spiral liner in ball mills,” Chinese Journal of Mechanical Engineering, vol. 30, no. 2, pp. 363-372, April 2017.

13. Xiaohang Jin, Yi Sun, Zijun Que, Yu Wang, and Tommy W. S. Chow, “Anomaly detection and fault prognosis for bearings,” IEEE Transactions on Instrumentation and Measurement, vol. 65, no. 9, pp. 2046-2054, Sept. 2016.

12. Yu Wang, Yizhen Peng, Yanyang Zi, Xiaohang Jin, and Kwok-Leung Tsui, “A two-stage data-driven based prognostic approach for bearing degradation problem,” IEEE Transactions on Industrial Informatics, vol. 12, no.3, pp. 924-932, June 2016. [ESI高被引论文]

11. Xiaohang Jin, Wei Qiao, Yayu Peng, Fangzhou Cheng, and Liyan Qu, “Quantitative evaluation of wind turbine faults under variable operational conditions,” IEEE Transactions on Industry Applications, vol. 52, no. 3, pp.2061-2069, May/June. 2016.

10. Weiming Jiang, Zhao Zhang, Fanzhang Li, Li Zhang, Mingbo Zhao, and Xiaohang Jin, “Joint label consistent dictionary learning and adaptive label prediction for semi-supervised machine fault classification,” IEEE Transactions on Industrial Informatics, vol. 12, no. 1, pp. 248-256, Feb. 2016.

09. Sandeep Menon, Xiaohang Jin, Tommy W. S.Chow, and Michael Pecht, “Evaluating covariance in prognostic and system health management applications,” Mechanical Systems and Signal Processing, vol. 58-59,pp.206-217, June 2015.

08. Xiaohang Jin, Tommy W. S. Chow, Yi Sun, Jihong Shan, and Bill C.P. Lau, “Kuiper test and autoregressive model-based approach for wireless sensor network fault diagnosis,” Wireless Networks, vol. 21, no. 3, pp. 829-839, 2015.

07. Mingbo Zhao, Xiaohang Jin, Zhao Zhang, and Bing Li, “Fault diagnosis of rolling element bearing via discriminant subspace learning: Visualization and classification,” Expert Systems with Applications, vol. 41, no. 7, pp. 3391-3401, June 2014.

06. Xiaohang Jin, Mingbo Zhao, Tommy W. S. Chow, and Michael Pecht, “Motor bearing fault diagnosis using trace ratio linear discriminant analysis,” IEEE Transactions on Industrial Electronics, vol. 61, no. 5, pp. 2441-2451, May 2014. [ESI高被引论文]

05. Xiaohang Jin, Tommy W. S. Chow, and Kwok-Leung Tsui, “Online anomaly detection for brushless dc motor using current monitoring technique,” International Journal of Performability Engineering, vol. 10, no. 3, pp. 263-271, May 2014.

04. Xiaohang Jin, Fang Yuan, Tommy W. S. Chow, and Mingbo Zhao, “Weighted local and global regressive mapping: A new manifold learning method for machine classification,” Engineering Applications of Artificial Intelligence, vol. 30, pp. 118-128, April 2014. 

03. Xiaohang Jin and Tommy W. S. Chow, “Anomaly detection of cooling fan and fault classification of induction motor using Mahalanobis-Taguchi system,” Expert Systems with Applications, vol. 40,no. 15, pp. 5787-5795, Nov. 2013.

02. Yu Wang, Xiaohang Jin, Shengxiang Chen, Xiongfei Wei, and Kwok-Leung Tsui, “Effect of low-frequency vibration in Z-direction(Out-of-plane) on slider dynamics,” IEEE Transactions on Magnetics, vol. 49, no.9, pp. 4977-4981, Sep. 2013.

01. Xiaohang Jin, Eden W. M. Ma, L. L.Cheng, and Michael Pecht, “Health monitoring of cooling fans based on Mahalanobis distance with mRMR feature selection,” IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 8, pp. 2222-2229, Aug. 2012.

中文期刊论文

21. 金晓航, 杨昊旋, 郭远晶, 张杰, 彭光健, 翁泽宇. 基于本征正交分解的风电机组结构在线状态监测. 太阳能学报. (录用)

20. 金晓航, 秦治伟, 郭远晶, 单继宏. 基于改进劣化度模型的风电机组日常运行状态评估. 太阳能学报. vol. 44, no. 1, pp. 237-244, Jan. 2023.

19. 郭远晶, 金晓航, 魏燕定, 杨友东. 基于S变换与变分模态提取的旋转机械故障诊断方法. 振动工程学报. vol. 35, no. 5, pp. 1289-1298, Oct. 2022.

18. 金晓航, 泮恒拓, 许壮伟, 孙毅, 刘伟江. 基于SCADA数据和单分类简化核极限学习机的风电机组发电机状态监测. 计算机集成制造系统. vol. 28, no. 8, pp. 2408-2418, August 2022.

17. 金晓航, 王宇, Bin Zhang. 工业大数据驱动的故障预测与健康管理. 计算机集成制造系统. vol. 28, no. 5, pp. 1314-1336, May 2022.

16. 金晓航, 泮恒拓, 徐正国. 数据驱动的风电机组变桨系统状态监测. 太阳能学报. vol. 43, no. 4, pp. 409-417, April 2022.

15. 金晓航, 许壮伟, 孙毅, 单继宏. 基于SCADA数据和稀疏自编码神经网络的风电机组在线运行状态监测. 太阳能学报. vol. 42, no. 6, pp. 321-328, June 2021.

14. 郭远晶, 金晓航, 魏燕定, 杨友东. 改进TSA降噪与平方包络谱分析的故障特征提取. 振动工程学报. vol. 34, no. 2, pp. 402-410, Apr. 2021.

13. 金晓航, 李建华, 郭远晶, 贾虹. 基于二元混合随机过程的轴承剩余寿命预测. 高技术通讯. vol. 30, no. 12, pp. 1284-1291, Dec. 2020.

12. 郭远晶, 魏燕定, 金晓航. 时频谱相似性度量的故障特征提取方法. 振动与冲击. vol. 39, no. 12, pp. 70-77, June 2020.

11. 金晓航, 许壮伟, 孙毅, 单继宏, 王欣. 基于生成对抗网络的风电机组在线状态监测. 仪器仪表学报. vol. 41, no. 4, pp. 68-76, April 2020.

10. 胡世鸣, 翁泽宇, 翁聪, 金晓航, 周旭, 黄德杰. 轮毂轴承实验中泥浆管道沉积问题研究. 高技术通讯. vol. 30, no. 4, pp. 415-423, Apr. 2020.

09. 金晓航, 李建华, 孙毅. 基于二元维纳过程的轴承剩余寿命预测. 仪器仪表学报. vol. 39, no. 6, pp. 89-95, June 2018.

08. 梁曼, 孙毅, 单继宏, 金晓航. 颤振球磨机中颗粒群粒度特性影响因素试验研究. 机械工程学报. vol. 54, no. 7, pp. 205-215, April 2018.

07. 郭远晶, 魏燕定, 金晓航, 杨友东. 频谱密度函数相似性比较的齿轮箱故障诊断. 振动工程学报. vol. 31, no. 1, pp. 157-164, Feb. 2018.

06. 郭远晶, 魏燕定, 金晓航, 杨友东. 基于S变换谱核密度估计的齿轮故障诊断. 仪器仪表学报. vol. 38, no. 6, pp. 1432-1439, June 2017.

05. 金晓航, 孙毅, 单继宏, 吴根勇. 风力发电机组故障诊断与预测技术研究综述. 仪器仪表学报. vol. 38, no. 5, pp. 1041-1053, May 2017.

04. 毛亚郞, 孙毅, 计时鸣, 单继宏, 金晓航. 基于单次料层冲击破碎质量模型的球磨选择函数. 中国机械工程. vol. 28, no. 1, pp. 168-172, Jan. 2017.

03. 阙子俊, 金晓航, 孙毅. 基于UKF的轴承剩余寿命预测方法研究. 仪器仪表学报. vol. 37, no. 9, pp. 2036-2043, Sept. 2016.

02. 梁曼, 孙毅, 纪朋朋, 单继宏, 金晓航. 正多边形角螺旋衬板对球磨机粉磨效率影响的数值分析. 机械工程学报. vol. 51, no. 17, pp. 203-212, Sep. 2015.

01. 金晓航, 刘永文, 苏明. 带中间冷却和回热的燃气轮机动态性能的研究. 动力工程. vol. 26, no. 3, pp. 326-328, June 2006.

中文教学课程

《工程图学》（国家级一流本科课程）

《机械制图》

《产品形体建模与创新设计》

《智能运维与健康管理》

英文教学课程

《工程图学》(留学生)

《产品形体建模与创新设计》(留学生)

《零件测绘技术与实践》(留学生)

《可靠性工程》(留学生)

Xiaohang JIN (金晓航), Ph.D.

Office

College of Mechanical Engineering, Zhejiang University of Technology

288 Liuhe Road, Xihu District, Hangzhou, China 310023

Email: xhjin@ieee.org

Positions

Associate Professor, College of Mechanical Engineering, Zhejiang University of Technology

Education

City University of Hong Kong, Ph.D., Electronic Engineering.

Shanghai Jiao Tong University, M.S., Power Machinery and Engineering.

Zhejiang University of Technology, B.S., Mechanical Engineering and Automation.

Impact Factor

Google Scholar Citations: 2088

Google Scholar h-index: 20

Google Scholar i10-index: 26

https://scholar.google.com.sg/citations?user=BTvLcgwAAAAJ&hl=en

Selected Awards and Honors

World's Top 2% Scientists (October 2023)

Talent Cultivation target of “14th Five-Year Plan” High-level Talent Cultivation Plan of Zhejiang University of Technology (June 2023)

Excellent teacher award of Zhejiang University of Technology in 2022 (April 2023)

Excellent teaching performance of College of Mechanical Engineering, Zhejiang University of Technology in 2022 (April 2023)

Excellent works award of the first young teachers science and technology achievement exhibition, College of Mechanical Engineering, Zhejiang University of Technology (February 2023)

Excellent teacher award of College of Mechanical Engineering, Zhejiang University of Technology in 2022 (January 2023)

Excellent teaching performance of College of Mechanical Engineering, Zhejiang University of Technology in 2020 (March 2021)

Excellent teaching performance of College of Mechanical Engineering, Zhejiang University of Technology in 2018 (April 2019)

“Outstanding Young Teacher (Young Talent Support Program)” of Zhejiang University of Technology in 2016

Excellent scientific and technological work award, College of Mechanical Engineering, Zhejiang University of Technology in 2015 (February 2016)

Research Tuition Scholarships, City University of Hong Kong, 2012-2013.

Outstanding Academic Performance Award, City University of Hong Kong, 2012 and 2013.

Personal Development Fund, Gulou District, Nanjing, 2010.

Outstanding Graduate Award, Shanghai Jiao Tong University, 2006.

Outstanding Graduate Award, Department of Education of Zhejiang Province, 2003.

Outstanding Student Scholarship, Zhejiang University of Technology, 2000-2002.

Publications

Peer Reviewed Articles

1.Yuanming Zhang, Weiyue Zhou, Jiacheng Huang, Xiaohang Jin, and Gang Xiao, “Temporal knowledge graph informer network for remaining useful life prediction,” IEEE Transactions on Instrumentation and Measurement, Early Access.

2.Zian Chen, Xiaohang Jin, Ziqian Kong, Feng Wang, and Zhengguo Xu, “Global and local information integrated network for remaining useful life prediction,” Engineering Applications of Artificial Intelligence, Early Access.

3.Zijun Que, Xiaohang Jin, Zhengguo Xu and Chang Hu, “Remaining useful life prediction based on incremental learning,” IEEE Transactions on Reliability, Early Access.

4.Chenli Shi, Ziqi Wang, Xiaohang Jin, Zhengguo Xu, Zhangsheng Wang, and Peng Shen, “A novel three-stage multi-population evolutionary algorithm for constrained multi-objective optimization problems,” Complex & Intelligent Systems, 2023. https://doi.org/10.1007/s40747-023-01181-6.

5.Ziqi Wang, Xiaohang Jin, and Zhengguo Xu, “An adaptive condition monitoring method of wind turbines based on multivariate state estimation technique and continual learning,” IEEE Transactions on Instrumentation and Measurement, vol. 72, Art no. 3513009, April 2023. 

6.Ziqian Kong, Xiaohang Jin, Zhengguo Xu, and Zian Chen, “A contrastive learning framework enhanced by unlabeled samples for remaining useful life prediction,” Reliability Engineering & System Safety, vol. 234, Article 109163, June 2023. doi.org/10.1016/j.ress.2023.109163.

7.Xiaohang Jin, Xiaoying Zhang, Xu Cheng, Guoqian Jiang, Lesedi Masisi, and Wei Huang, “A physics-based and data-driven feature extraction model for blade icing detection of wind turbines,” IEEE Sensors Journal, vol. 23, no. 4, pp. 3944-3954, Feb. 2023. doi: 10.1109/JSEN.2023.3234151.

8.Xiaohang Jin, Hao Wang, Ziqian Kong, Zhengguo Xu, and Wei Qiao, “Condition monitoring of wind turbine based on SCADA data and feature Transfer learning,” IEEE Access, vol. 11, pp. 9441-9450, 2023. doi: 10.1109/ACCESS.2023.3240306.

9.Xiaohang Jin, Hengtuo Pan, Chengzuo Ying, Ziqian Kong, Zhengguo Xu, and Bin Zhang, “Condition monitoring of wind turbine generator based on Transfer learning and one-class classifier,” IEEE Sensors Journal, vol. 22, no. 24, pp. 24130-24139, Dec. 2022. doi: 10.1109/JSEN.2022.3218054.

10.Ziqian Kong, Xiaohang Jin, Zhengguo Xu, and Bin Zhang, “Spatio-temporal fusion attention: A novel approach for remaining useful life prediction based on graph neural network,” IEEE Transactions on Instrumentation and Measurement, vol. 71, Art no. 3515912, 2022.

11.Yuanjing Guo, Youdong Yang, Shaofei Jiang, Xiaohang Jin, and Yanding Wei, “Rolling bearing fault diagnosis based on successive variational mode decomposition and the EP index,” Sensors, vol. 22, no. 10: 3889, 2022.

12.Yuanjing Guo, Shaofei Jiang, Youdong Yang, Xiaohang Jin, and Yanding Wei, “Gearbox fault diagnosis based on improved variational mode extraction,” Sensors, vol. 22, no. 5: 1779, 2022.

13.Zijun, Que, Xiaohang Jin, and Zhengguo Xu, “Remaining useful life prediction for bearings based on a gate recurrent unit,” IEEE Transactions on Instrumentation and Measurement, vol. 70, Art no. 3511411, 2021.

14.Xiaohang Jin, Zhuangwei Xu, and Wei Qiao, “Condition monitoring of wind turbine generators using SCADA data analysis,” IEEE Transactions on Sustainable Energy, vol. 12, no. 1, pp. 202-210, Jan. 2021. [ESI Highly Cited Papers]

15.Xiaohang Jin, Jicong Fan, and Tommy W. S. Chow, “Fault detection for rolling-element bearings using multivariate statistical process control methods,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 9, pp. 3128-3136, Sept. 2019.

16.Xiaohang Jin, Zijun Que, Yi Sun, Yuanjing Guo, and Wei Qiao, “A data-driven approach for bearing fault prognostics,” IEEE Transactions on Industry Applications, vol. 55, no. 4, pp.3394-3401, July/August 2019.

17.Xiaohang Jin, Fangzhou Cheng, Yayu Peng, Wei Qiao, and Liyan Qu, “Drivetrain gearbox fault diagnosis: vibration- and current-based approaches,” IEEE Industry Applications Magazine, vol. 24, no. 6, pp. 56-66, Dec. 2018.

18.Xiaohang Jin, Yu Wang, Tommy W. S. Chow, and Yi Sun, “MD-based approaches for system health monitoring: a review,” IET Science, Measurement & Technology, vol. 11, no. 4, pp. 371-379, July 2017.

19.Yi Sun, Man Liang, Xiaohang Jin, Pengpeng Ji, Jihong Shan, “Experimental and modeling study of the regular polygon angle-spiral liner in ball mills,” Chinese Journal of Mechanical Engineering, vol. 30, no. 2, pp. 363-372, April 2017.

20.Xiaohang Jin, Yi Sun, Zijun Que, Yu Wang, and Tommy W. S. Chow, “Anomaly detection and fault prognosis for bearings,” IEEE Transactions on Instrumentation and Measurement, vol. 65, no. 9, pp. 2046-2054, Sept. 2016.

21.Yu Wang, Yizhen Peng, Yanyang Zi, Xiaohang Jin, and Kwok-Leung Tsui, “A two-stage data-driven based prognostic approach for bearing degradation problem,” IEEE Transactions on Industrial Informatics, vol. 12, no.3, pp. 924-932, June 2016. [ESI Highly Cited Papers]

22.Xiaohang Jin, Wei Qiao, Yayu Peng, Fangzhou Cheng, and Liyan Qu, “Quantitative evaluation of wind turbine faults under variable operational conditions,” IEEE Transactions on Industry Applications, vol. 52, no. 3, pp.2061-2069, May/June. 2016.

23.Weiming Jiang, Zhao Zhang, Fanzhang Li, Li Zhang, Mingbo Zhao, and Xiaohang Jin, “Joint label consistent dictionary learning and adaptive label prediction for semi-supervised machine fault classification,” IEEE Transactions on Industrial Informatics, vol. 12, no. 1, pp. 248-256, Feb. 2016.

24.Sandeep Menon, Xiaohang Jin, Tommy W. S.Chow, and Michael Pecht, “Evaluating covariance in prognostic and system health management applications,” Mechanical Systems and Signal Processing, vol. 58-59,pp.206-217, June 2015.

25.Xiaohang Jin, Tommy W. S. Chow, Yi Sun, Jihong Shan, and Bill C.P. Lau, “Kuiper test and autoregressive model-based approach for wireless sensor network fault diagnosis,” Wireless Networks, vol. 21, no. 3, pp. 829-839, 2015.

26.Mingbo Zhao, Xiaohang Jin, Zhao Zhang, and Bing Li, “Fault diagnosis of rolling element bearing via discriminant subspace learning: Visualization and classification,” Expert Systems with Applications, vol. 41, no. 7, pp. 3391-3401, June 2014.

27.Xiaohang Jin, Mingbo Zhao, Tommy W. S. Chow, and Michael Pecht, “Motor bearing fault diagnosis using trace ratio linear discriminant analysis,” IEEE Transactions on Industrial Electronics, vol. 61, no. 5, pp. 2441-2451, May 2014. [ESI Highly Cited Papers]

28.Xiaohang Jin, Tommy W. S. Chow, and Kwok-Leung Tsui, “Online anomaly detection for brushless dc motor using current monitoring technique,” International Journal of Performability Engineering, vol. 10, no. 3, pp. 263-271, May 2014.

29.Xiaohang Jin, Fang Yuan, Tommy W. S. Chow, and Mingbo Zhao, “Weighted local and global regressive mapping: A new manifold learning method for machine classification,” Engineering Applications of Artificial Intelligence, vol. 30, pp. 118-128, April 2014. 

30.Xiaohang Jin and Tommy W. S. Chow, “Anomaly detection of cooling fan and fault classification of induction motor using Mahalanobis-Taguchi system,” Expert Systems with Applications, vol. 40, no. 15, pp. 5787-5795, Nov. 2013.

31.Yu Wang, Xiaohang Jin, Shengxiang Chen, Xiongfei Wei, and Kwok-Leung Tsui, “Effect of low-frequency vibration in Z-direction(Out-of-plane) on slider dynamics,” IEEE Transactions on Magnetics, vol. 49, no.9, pp. 4977-4981, Sep. 2013.

32.Xiaohang Jin, Eden W. M. Ma, L. L.Cheng, and Michael Pecht, “Health monitoring of cooling fans based on Mahalanobis distance with mRMR feature selection,” IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 8, pp. 2222-2229, Aug. 2012.

Peer Reviewed Articles in Chinese (Indexed by Engineering Village):

1.Jin Xiaohang, Qin Zhiwei, Guo Yuanjing, and Shan Jihong, “Evaluation of wind turbines daily operation conditions based on improved degradation model,” Taiyangneng Xuebao/Acta Energiae Solaris Sinica, vol. 44, no. 1, pp. 239-246, Jan. 2023.

2.Guo Yuanjing, Jin Xiaohang, Wei Yanding, and Yang YouDong, “Fault diagnosis method of rotating machinery using variational mode extraction guided by S transform,” Zhendong Gongcheng Xuebao/Journal of Vibration Engineering, vol. 35, no. 5, pp. 1289-1298, Oct. 2022.

3.Jin Xiaohang, Wang Yu, and Zhang Bin, “Industrial big data-driven fault prognostics and health management,” Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, vol. 28, no. 5, pp. 1314-1336, May 2022.

4.Jin Xiaohang, Pan Hengtuo, and Xu Zhengguo, “Condition monitoring of wind turbine pitch system using data-driven approach,” Taiyangneng Xuebao/Acta Energiae Solaris Sinica, vol. 43, no. 4, pp. 409-417, Apr. 2022.

5.Jin Xiaohang, Pan Hengtuo, Xu Zhuangwei, Sun Yi, and Liu Weijiang, “Condition monitoring of wind turbine generators using SCADA data and OC-RKELM,” Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, vol. 28, no. 8, pp. 2408-2418, Aug. 2022.

6.Jin Xiaohang, Xu Zhuangwei, Sun Yi, and Shan Jihong, “Online condition monitoring for wind turbines based on SCADA data analysis and sparse auto-encoder neural network,” Taiyangneng Xuebao/Acta Energiae Solaris Sinica, vol. 42, no. 6, pp. 321-328, June 2021.

7.Guo Yuanjing, Jin Xiaohang, Wei Yanding, and Yang Youdong, “Fault feature extraction based on improved TSA denoising and squared envelope spectrum,” Zhendong Gongcheng Xuebao/Journal of Vibration Engineering, vol. 34, no. 2, pp. 402-410, Apr. 2021.

8.Guo Yuanjing, Wei Yanding, Jin Xiaohang, and Lin Yong, “Fault feature extraction from time-frequency spectrum by using similarity measurement,” Zhendong yu Chongji/Journal of Vibration and Shock, vol. 39, no. 12, pp. 70-77, June 2020.

9.Jin Xiaohang, Xu Zhuangwei, Sun Yi, Shan Jihong, and Wang Xin, “Online condition monitoring of wind turbine based on generative adversarial network,” Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, vol. 41, no. 4, pp. 68-76, Apr. 2020.

10.Guo Yuanjing, Wei Yanding, Jin Xiaohang, and Yang Youdong, “Gearbox fault diagnosis using similarity comparison of frequency spectrum density function,” Zhendong Gongcheng Xuebao/Journal of Vibration Engineering, vol. 31, no. 1, pp. 157-164, Feb. 2018.

11.Liang Man, Sun Yi, Shan Jihong, and Jin Xiaohang, “Experiment on the influence of features of the broken particle size in a flutter ball mill,” Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, vol. 54, no. 7, pp. 205-215, Apr. 2018.

12.Jin Xiaohang, Li Jianhua, and Sun Yi, “Bearing remaining useful life prediction based on two-dimensional wiener process,” Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, vol. 39, no. 6, pp. 89-95, June 2018.

13.Jin Xiaohang, Sun Yi, Shan Jihong, and Wu Genyong, “Fault diagnosis and prognosis for wind turbines: An overview,” Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, vol. 38, no. 5, pp. 1041-1053, May 2017.

14.Mao Yalang, Sun Yi, Ji Shiming, Shan Jihong, and Jin Xiaohang, “Selection function based on single-impact breakage mass model of particle beds in ball mills,” Zhongguo Jixie Gongcheng/China Mechanical Engineering, vol. 28, no. 1, pp. 2-6, Jan. 2017.

15.Guo Yuanjing, Wei Yanding, Jin Xiaohang, and Yang Youdong, “Gear fault diagnosis based on kernel density estimation of S transform spectrum,” Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, vol. 38, no. 6, pp. 1432-1439, June 2017.

16.Que Zijun, Jin Xiaohang, and Sun Yi, “Remaining useful life prediction for bearings with the unscented Kalman filter-based approach,” Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, vol. 37, no. 9, pp. 2036-2043, Sept. 2016.

17.Liang Man, Sun Yi, Ji Pengpeng, Shan Jihong, and Jin Xiaohang, “Numerical analysis on regular polygon angle-spiral liners design in a ball mill,” Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, vol. 51, no. 17, pp. 203-212, Sept. 2015.

18.Jin Xiaohang, Liu Yongwen, and Su Ming, “Dynamic behavior of a inter-cooled gas turbine set with waste heat recuperator,” Dongli Gongcheng/Power Engineering, vol. 26, no. 3, pp. 326-328+446, June 2006.

Peer Reviewed Conference Proceeding Articles:

1.Xiaohang Jin, Shengye Lv, Ziqian Kong, Hongchun Yang, Yuanming Zhang, and Yuanjing Guo, “Prior knowledge embedded networks for condition monitoring of wind turbine,” in The 5th International Conference on Reliability Systems Engineering, September 21-23, 2023, Hefei, China.

2.Xiaohang Jin, Zijun Que, and Yi Sun, “Development of vibration-based health indexes for bearing remaining useful life prediction,” in IEEE 2019 Prognostics & System Health Management Conference, October 25-27, 2019, Qingdao, China.

3.Xiaohang Jin, Zijun Que, Yi Sun, Yuanjing Guo, and Wei Qiao, “A data-driven approach for bearing fault prognostics,” in Proceedings of IEEE IAS 53rd Annual Meeting, Portland, OR, USA, Sept. 23-27, 2018, pp. 1-8.

4.Xiaohang Jin, Fangzhou Cheng, Yayu Peng, Wei Qiao, and Liyan Qu, “A comparative study on vibration- and current-based approaches for driventrain gearbox fault diagnosis,” in Proceedings of IEEE IAS 51rd Annual Meeting, Portland, OR, USA, Oct. 2-6, 2016, pp. 1-8.

5.Xiaohang Jin, Yayu Peng, Fangzhou Cheng, Wei Qiao, and Liyan Qu, “Quantitative evaluation of wind turbine faults under variable operational conditions,” in Proceedings of IEEE IAS 50th Annual Meeting, Dallas, TX, USA, Oct. 18-22, 2015, pp. 1-8.

6.Yu Wang, Yizhen Peng, Yanyang Zi, Xiaohang Jin, and Kwok-Leung Tsui, “An integrated Bayesian approach to prognositics of the remaining useful life and its application on bearing degradation problem,” in IEEE International Conference on Industrial Informatics INDIN’15, July 22-24, 2015, Cambridge, UK.

7.Xiaohang Jin, Yi Sun, Jihong Shan, Yu Wang, and Zhengguo Xu, “Health monitoring and fault detection using wavelet packet technique and multivariate process control method,” in IEEE 2014 Prognostics & System Health Management Conference, August 24-27, Zhangjiajie City, China.

8.Xiaohang Jin, Yi Sun, and Jihong Shan, “PolSOM based approach for bearing fault diagnosis,” in IEEE 2014 Prognostics & System Health Management Conference, August 24-27, Zhangjiajie City, China.

9.Xiaohang Jin, Eden W. M. Ma, Tommy W. S. Chow, and Michael Pecht, “An investigation into fan reliability,” in IEEE 2012 Prognostics & System Health Management Conference, May 23-25, 2012, Beijing, China, MU3274.

10.Xiaohang Jin, Michael H. Azarian, Chunpiu Lau, LL Cheng, and Michael Pecht, “Physics-of-failure analysis of cooling fans,” in IEEE 2011 Prognostics & System Health Management Conference, 23-25 May 2011, Shenzhen, China, MU3174.

Professional Activities

Outstanding Reviewer

­Computer Integrated Manufacturing Systems (2020 and 2022)

­Chinese Journal of Scientific Instrument (2020 and 2021)

­IEEE Transactions on Instrumentation and Measurement (2018)

Reviewer

­IEEE Transactions on Industrial Electronics;

­IEEE Transactions on Industrial Informatics;

­IEEE Transactions on Device and Materials Reliability;

­IEEE Transactions on Instrumentation and Measurement;

­IEEE Embedded Systems Letters;

­IEEE Sensors Journal;

­IEEE Access;

­IET Science, Measurement & Technology;

­Mechanical Systems and Signal Processing;

­Engineering Applications of Artificial Intelligence;

­Reliability Engineering and System Safety;

­Electric Power Components and Systems;

­Pattern Analysis and Applications;

­Journal of Mechanical Science and Technology;

­Microelectronics Reliability;

­PLOS ONE;

­Sensors;

­Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (Sage publications);

­Advances in Mechanical Engineering;

­Shock and Vibration;

­International Journal of Prognostics and Health Management;

­2012 IEEE PHM conference (Beijing), 2013 IEEE PHM conference (Maryland), and annual conference of the prognostics and health management society 2013 (New Orleans, LA, USA), 2014 IEEE PHM conference (Hunan, China), 2016 PHM conference (USA), 2017 IEEE PHM conference (Harbin, China).

Senior Member, Institute of Electrical and Electronic Engineers (IEEE), 2017-present

Associate Editor, Wind Energy Science, 2023-present

Member, American Society for Quality (ASQ), 2009

Certified Reliability Engineer, American Society for Quality (ASQ), October 2009-December 2012