header
邵海东

发布于:暂无信息 点击数:345

暂无信息

基本信息

图片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:助理教授、岳麓学者晨星岗B
办公室:学院楼409
Email:  hdshao@hnu.edu.cnhdshao@mail.nwpu.edu.cn


教育背景

2009年9月-2013年7月,西北工业大学航空学院,电气工程及其自动化,学士
2013年9月-2015年9月,西北工业大学航空学院,载运工具运用工程,硕士,导师:姜洪开 教授
2015年9月-2018年12月,西北工业大学航空学院,载运工具运用工程,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(岳麓学者晨星岗B)

学术兼职

Mechanical Systems and Signal Processing、IEEE Transactions on Industrial Electronics、IEEE Transactions on Industrial Informatics、Knowledge-Based Systems、ISA Transactions、IEEE Transactions on Systems, Man, and Cybernetics、IEEE Access、Neurocomputing、Measurement、 Computers in Industry、Measurement Science and Technology等国际期刊审稿人

研究领域

深度学习与信号处理,状态监测与健康管理,智能诊断与寿命预测

科研项目

近五年主持及参与科研项目
[1]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(主持)
[2]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(参与)
[3]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(参与)
[4]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(参与)
[5]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(参与)
[6]航空科学基金项目:航空发动机XXXX早期故障诊断技术研究(参与)
[7]航空科学基金项目:基于机器学习的XX振动环境预计方法研究(参与)
[8]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(参与)


学术成果

高被引论文
[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大类一区, Top期刊, IF=7.050, ESI高被引)
[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=4.370, ESI高被引)
[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=4.396, ESI高被引)
[4] 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大类三区, IF=1.685, IOP Publishing中国区高被引)

期刊论文
[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大类一区, Top期刊, IF=7.050, ESI高被引)
[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=4.370, ESI高被引)
[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=4.396, ESI高被引)
[4] 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=4.370)
[5] 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=4.370)
[6] 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=4.370)
[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=4.396)
[8] 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=3.370)
[9] 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大类三区, IF=2.850)
[10] 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=1.685, IOP Publishing中国区高被引)
[11]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=1.426)
[12] 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=1.685)
[13]Wang Fu’an, 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=1.685)
[14] 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=2.796)

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

会议论文
[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.
[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.
[3] 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.

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. 授权号:ZL 201610259840.9.


奖励与荣誉

[1] 2018年,获全国宝钢教育基金“优秀学生特等奖”(全国25人)
[2] 2018年,获教育部博士研究生“国家奖学金”
[3] 2018年,获西北工业大学“优秀研究生标兵”
[4] 2018年,获西北工业大学优秀研究生“学术之星”
[5] 2017年,获教育部博士研究生“国家奖学金”
[6] 2017年,获西北工业大学优秀研究生“学术之星”
[7] 2016年,获教育部博士研究生“国家奖学金”