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邵海东

发布于:2019-12-10 星期二 11:11:29 点击数:6044

湖南大学岳麓学者,助理教授,硕士生导师,研究方向为机电设备健康管理与智能运维。主持国家自然科学基金青年科学基金项目1项,湖南省自然科学基金青年科学基金项目1项,中央高校基本科研业务费项目1项。作为主要成员参与了国家自然科学基金重大研究计划、面上项目、军委装备发展部预研基金项目等课题。

目前已发表SCI/EI期刊论文26篇,第一作者/通讯作者SCI期刊论文17篇:中科院一区Top5篇,中科院二区7篇,1篇入选ESI热点论文,7篇入选ESI高被引论文,1篇入选IOP Publishing中国区高被引论文。第一作者/通讯作者发表《机械工程学报》期刊论文2篇,发表EI国际会议论文4篇,授权国家发明专利1项。


基本信息

NN(AM2163P$QDD(BXLP(2}1.png

姓名:邵海东

出生年月:1990年3月

系别:机电工程系

职称/职务:岳麓学者、助理教授、硕士生导师

办公室:机械院楼409

Email: hdshao@hnu.edu.cn、hdshao@mail.nwpu.edu.cn

ResearchGate:https://www.researchgate.net/profile/Haidong_Shao 



教育背景

2006年09月-2009年06月,浙江省镇海中学,高中

2009年09月-2013年07月,西北工业大学,航空学院,电气工程及其自动化,学士

2013年09月-2015年09月,西北工业大学,航空学院,载运工具运用工程,工学硕士,导师:姜洪开 教授

2015年09月-2018年12月,西北工业大学,航空学院,载运工具运用工程,工学博士,导师:姜洪开 教授




工作履历

2018年12月-至今,湖南大学机械与运载工程学院,助理教授

2018年12月-至今,湖南大学,岳麓学者(晨星岗B)

2019年06月-至今,湖南大学机械与运载工程学院,硕士生导师

2019年09月-2019年10月, Division of Operation, Maintenance and Acoustics, Luleå University of Technology, Visiting Scholar. 合作教授:Janet (Jing) Lin, Uday Kumar


学术兼职

Reviewers of 

IEEE Transactions on Industrial Electronics、IEEE Transactions on Industrial Informatics、IEEE Transactions on Systems, Man, and Cybernetics: Systems、Mechanical Systems and Signal Processing、Knowledge-Based Systems、ISA Transactions、Computers in Industry、Journal of Manufacturing Systems、International Journal of Electrical Power and Energy Systems、 Neurocomputing、Measurement、IEEE Sensors Journal、Journal of Process Control、Measurement Science and Technology、Applied Intelligence、International Journal of Acoustics and Vibration、Part C: Journal of Mechanical Engineering Science、IEEE Access、Sensors、Advances in Mechanical Engineering、 Shock and Vibration


《振动与冲击》期刊审稿人


Organizer of

Special Session “PHM for transportation” of IEEE Global Reliability and Prognostics & Health Management Conference (PHM 2020)

Special Session “Intelligent sensing and data analytics for smart manufacturing” of International Conference on Sensing, Measurement and Data Analytics (ICSMD2020)





研究领域

研究方向:健康管理与智能运维,故障诊断与寿命预测,信号处理与深度学习

研究对象:大型机电液装备(航空发动机,直升机传动系统,列车牵引系统,风力发电机等)

欢迎自动化、机械工程、电气工程、动力工程、计算机科学等相关专业背景的学生讨论交流。


科研项目

近五年主持科研项目

[1]主持国家自然科学基金青年科学基金项目:基于深度生成对抗网络的直升机动力传动系统智能健康预示研究,51905160,2020年01月至2022年12月

[2]主持湖南省自然科学基金青年科学基金项目:多源参数驱动下航空发动机早期故障的集成深度迁移诊断方法研究,2020年01月至2022年12月

[3]主持中央高校基本科研业务费项目:基于深度学习和多源信息融合的机械故障智能诊断,531118010335,2019年01月至2022年12月

[4]主持西北工业大学博士论文创新基金重点资助项目:深度学习理论在飞行器故障预示中的应用研究,CX201710,2017年01月至2018年12月


近五年参与科研项目

[1]参与国家自然科学基金重大研究计划培育项目:航空发动机健康状态多源深度信息融合与智能预示研究

[2]参与国家自然科学基金面上项目:临近空间飞行器服役性能退化机理与健康自主感知方法研究

[3]参与国家自然科学基金面上项目:基于深度学习的飞行器故障不确定性评估与预测研究

[4]参与军委装备发展部预研基金项目:XXXX特征提取与定量诊断技术

[5]参与航空科学基金项目:航空发动机XXXX早期故障诊断技术研究

[6]参与航空科学基金项目:基于机器学习的XXXX振动环境预计方法研究

[7]参与中国商飞客服基金项目:民用飞机飞控系统状态监控及故障诊断技术研究

[8]参与中航工业西安航空计算技术研究所(631所)项目:XXXX PHM软件验证设备

[9]参与中国航天西安空间无线电技术研究所(504所)项目:XXXX监测平台研制


学术成果

ESI热点论文(ESI Hot Paper)

[1] Shao Haidong, Jiang Hongkai, Zhang Haizhou, et al. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing[J]. Mechanical Systems and Signal Processing, 2018, 100: 743-765. (SCI小类一区, Top期刊, IF=5.005, ESI热点论文,入选2019年5月期)


ESI高被引论文(ESI Highly Cited Paper)

[1] Shao Haidong, Jiang Hongkai, Zhang Haizhou, et al. Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network[J]. IEEE Transactions on Industrial Electronics, 2018, 65(3): 2727-2736. (SCI双一区, Top期刊, IF=7.503, ESI高被引论文)

[2] Shao Haidong, Jiang Hongkai, Zhao Huiwei, et al. A novel deep autoencoder feature learning method for rotating machinery fault diagnosis[J]. Mechanical Systems and Signal Processing, 2017, 95: 187-204. (SCI小类一区, Top期刊, IF=5.005, ESI高被引论文) 

[3] Shao Haidong, Jiang Hongkai, Wang Fuan, et al. An enhancement deep feature fusion method for rotating machinery fault diagnosis[J]. Knowledge-Based Systems, 2017, 119: 200-220. (SCI大类二区, IF=5.101, ESI高被引论文)

[4] Shao Haidong, Jiang Hongkai, Zhang Haizhou, et al. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing[J]. Mechanical Systems and Signal Processing, 2018, 100: 743-765. (SCI小类一区, Top期刊, IF=5.005, ESI高被引论文)

[5] Shao Haidong, Jiang Hongkai, Lin Ying, et al. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders[J]. Mechanical Systems and Signal Processing, 2018, 102: 278-297. (SCI小类一区, Top期刊, IF=5.005, ESI高被引论文)

[6] Shao Haidong, Jiang Hongkai, Li Xingqiu, et al. Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine[J]. Knowledge-Based Systems, 2018, 140: 1-14. (SCI大类二区, IF=5.101, ESI高被引论文)

[7] Shao Haidong, Jiang Hongkai, Zhang Xun, et al. Rolling bearing fault diagnosis using an optimization deep belief network[J]. Measurement Science and Technology, 2015, 26: 115002. (SCI小类三区, IF=1.861, ESI高被引论文)


IOP Publishing高被引论文(IOP Publishing Highly Cited Paper)

[1] Shao Haidong, Jiang Hongkai, Zhang Xun, et al. Rolling bearing fault diagnosis using an optimization deep belief network[J]. Measurement Science and Technology, 2015, 26: 115002. (SCI小类三区, IF=1.861, IOP Publishing高被引论文)



期刊论文(Journal Paper)


第一作者/通讯作者(First author and Corresponding author)

[1] Shao Haidong, Jiang Hongkai, Zhang Haizhou, et al. Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network[J]. IEEE Transactions on Industrial Electronics, 2018, 65(3): 2727-2736. (SCI双一区, Top期刊, IF=7.503, ESI高被引论文)

[2] Shao Haidong, Jiang Hongkai, Zhao Huiwei, et al. A novel deep autoencoder feature learning method for rotating machinery fault diagnosis[J]. Mechanical Systems and Signal Processing, 2017, 95: 187-204. (SCI小类一区, Top期刊, IF=5.005, ESI高被引论文) 

[3] Shao Haidong, Jiang Hongkai, Wang Fuan, et al. An enhancement deep feature fusion method for rotating machinery fault diagnosis[J]. Knowledge-Based Systems, 2017, 119: 200-220. (SCI大类二区, IF=5.101, ESI高被引论文)

[4] Shao Haidong, Jiang Hongkai, Zhang Haizhou, et al. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing[J]. Mechanical Systems and Signal Processing, 2018, 100: 743-765. (SCI小类一区, Top期刊, IF=5.005, ESI热点论文,入选2019年5月期)

[5] Shao Haidong, Jiang Hongkai, Lin Ying, et al. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders[J]. Mechanical Systems and Signal Processing, 2018, 102: 278-297. (SCI小类一区, Top期刊, IF=5.005, ESI高被引论文)

[6] Shao Haidong, Jiang Hongkai, Zhao Ke, et al. A novel tracking deep wavelet auto-encoder method for intelligent fault diagnosis of electric locomotive bearings[J]. Mechanical Systems and Signal Processing, 2018, 110: 193-209. (SCI小类一区, Top期刊, IF=5.005)

[7] Shao Haidong, Jiang Hongkai, Li Xingqiu, et al. Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine[J]. Knowledge-Based Systems, 2018, 140: 1-14. (SCI大类二区, IF=5.101, ESI高被引论文)

[8] Shao Haidong*, Cheng Junsheng, Jiang Hongkai, et al. Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing[J]. Knowledge-Based Systems, 2020, 188, 105022. (SCI大类二区, IF=5.101)

[9] Shao Haidong, Jiang Hongkai, Wang Fuan, et al. Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet[J]. ISA Transactions, 2017, 69: 187-201. (SCI大类二区, IF=4.343)

[10] Shao Haidong, Jiang Hongkai, Li Xingqiu, et al. Rolling bearing fault detection using continuous deep belief network with locally linear embedding[J]. Computers in Industry, 2018, 96: 27-39. (SCI大类二区, IF=4.769)

[11] Shao Haidong, Jiang Hongkai, Zhang Xun, et al. Rolling bearing fault diagnosis using an optimization deep belief network[J]. Measurement Science and Technology, 2015, 26: 115002. (SCI小类三区, IF=1.861, ESI高被引论文, IOP Publishing高被引论文)

[12] Shao Haidong, Lin Jing, Zhang Liangwei, et al. Compound fault diagnosis for a rolling bearing using adaptive DTCWPT with higher order spectra[J]. Quality Engineering, 2020, Accepted. (SCI小类三区, IF=1.24)

[13]He Zhiyi, Shao Haidong*, Wang Ping, et al. Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples[J]. Knowledge-Based Systems, 2020, 191, 105313. (SCI大类二区, IF=5.101)

[14]He Zhiyi, Shao Haidong*, Zhang Xiaoyang, et al. Improved Deep Transfer Auto-encoder for Fault Diagnosis of Gearbox under Variable Working Conditions With Small Training Samples[J]. IEEE Access, 2019, 7: 115368-115377. (SCI大类二区, IF=4.098)

[15]He Zhiyi, Shao Haidong*, Lin Jing, et al. Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder [J]. Measurement, 2020, 152, 107393. (SCI大类三区, IF= 2.791)

[16]He Zhiyi, Shao Haidong*, Cheng Junsheng, et al. Kernel flexible and displaceable convex hull based tensor machine for gearbox fault intelligent diagnosis with multi-source signals[J]. Measurement, 2020, Accepted. (SCI大类三区, IF= 2.791)

[17]Jiang Hongkai, Shao Haidong, Chen Xinxia, et al. A feature fusion deep belief network method for intelligent fault diagnosis of rotating machinery[J]. Journal of Intelligent & Fuzzy Systems, 2018, 34(6): 3513-3521. (SCI大类四区, IF=1.637)

[18]邵海东*, 张笑阳, 程军圣, 杨宇. 基于提升深度迁移自动编码器的轴承智能故障诊断[J]. 机械工程学报, 2020, Accepted. (EI, 中文顶级期刊)

[19]姜洪开, 邵海东, 李兴球. 基于深度学习的飞行器智能故障诊断方法[J]. 机械工程学报, 2019, 55(7), 27-34. (EI, 中文顶级期刊, 入选机械工程学报2019年第7期最受关注论文)



合作作者(Co-author)

[20] He Zhiyi, Shao Haidong, Cheng Junsheng, et al. Support tensor machine with dynamic penalty factors and its application to the fault diagnosis of rotating machinery with unbalanced data[J]. Mechanical Systems and Signal Processing, 2020, 141, 106441. (SCI小类一区, Top期刊, IF=5.005)

[21]Zhao Ke, Shao Haidong. Intelligent Fault Diagnosis of Rolling Bearing Using Adaptive Deep Gated Recurrent Unit[J]. Neural Processing Letters, 2020, 51, 1165-1184. (SCI大类三区, IF=2.591)

[22]Wei Dongdong, Jiang Hongkai, Shao Haidong, et al. An optimal variational mode decomposition for rolling bearing fault feature extraction[J]. Measurement Science and Technology, 2019, 30: 055004. (SCI小类三区, IF=1.861)

[23] Jiang Hongkai, Li Xingqiu, Shao Haidong, et al. Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network[J]. Measurement Science and Technology, 2018, 29: 065107. (SCI小类三区, IF=1.861)

[24]Wang Fuan, Jiang Hongkai, Shao Haidong, et al. An adaptive deep convolutional neural network for rolling bearing fault diagnosis[J]. Measurement Science and Technology 28 (2017) 095005. (SCI小类三区, IF=1.861)

[25] Li Xingqiu, Jiang Hongkai, Xiong Xiong, Shao Haidong. Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network[J]. Mechanism and Machine Theory 133 (2019) 229-249. (SCI小类二区, IF=3.535)

[26] Zhao Xianzhu, Cheng Junsheng, Wang ping, He Zhiyi, Shao Haidong, Yang Yu. A novelty detection scheme for rolling bearing based on multiscale fuzzy distribution entropy and hybrid kernel convex hull approximation[J]. Measurement 156 (2020) 107589. (SCI大类三区, IF= 2.791)



会议论文(Conference Paper)

[1]Shao Haidong*, Jiang Hongkai. Unsupervised feature learning of gearbox fault using stacked wavelet auto-encoder[C]. The 9th Annual IEEE International Conference on Prognostics and Health Management (ICPHM), Seattle, USA, 2018: 1-8. (EI)

[2]Shao Haidong, Jiang Hongkai, Zhao Huiwei, et al. Aircraft electromechanical system fault diagnosis based on deep learning[C]. The 29th International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM), Xi’an, China, 2016: 1-6. (EI)

[3] Shao Haidong, Jiang Hongkai. Research on semi-active suspension vibration control using magneto-rheological damper[C]. Proceedings of the First Symposium on Aviation Maintenance and Management-Volume Ⅱ. Springer Berlin Heidelberg, Xi’an, China, 2014: 441-447. (EI)

[4] Jiang Hongkai, Shao Haidong, Chen Xinxia, et al. Aircraft fault diagnosis based on deep belief network[C]. The International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Shanghai, China, 2017: 123-127. (EI)


授权专利

[1] 姜洪开, 邵海东, 张雪莉, 王福安. 一种基于连续深度置信网络的滚动轴承故障预测方法. 公开号:CN 105973594B, 授权号:ZL 201610259840.9. 

奖励与荣誉

国际级

[1] 2018年,Top Cited Author Award 2018(英国物理学会出版社IOP Publishing)


国家级

[1] 2018年,全国宝钢“优秀学生特等奖”(宝钢教育基金会,全国25人,相关报道:

https://news.nwpu.edu.cn/info/1002/60025.htm)

[2] 2018年,博士研究生“国家奖学金”(中华人民共和国教育部)

[3] 2017年,博士研究生“国家奖学金”(中华人民共和国教育部)

[4] 2016年,博士研究生“国家奖学金”(中华人民共和国教育部)


校级

[1] 2019年,新聘教师微格教学比赛校三等奖(湖南大学)

[2] 2019年,研究生“优秀毕业生”(西北工业大学)

[3] 2018年,“优秀研究生标兵”(西北工业大学)

[4] 2018年,优秀研究生“学术之星”(西北工业大学)

[5] 2017年,优秀研究生“学术之星”(西北工业大学)