Released Time:2019-12-10 星期二 11:11:29 Hits:49575
Contact InfoCollege of Mechanical and Vehicle Engineering, Hunan University
Lushan Road (S), Yuelu District, Changsha, 410082
hdshao@hnu.edu.cn
2018 – present: Assistant Professor, Hunan University, College of Mechanical and Vehicle Engineering
2018: PhD Vehicle Operation Engineering, School of Aeronautics, Northwestern Polytechnical University
2015: Master Vehicle Operation Engineering, School of Aeronautics, Northwestern Polytechnical University
2013: Bachelor Electrical Engineering and Automation, School of Aeronautics, Northwestern Polytechnical University
Deep learning, Signal processing, Health Monitoring, Fault Diagnosis
2019 | Outstanding graduates, Northwestern Polytechnical University. |
2018 | Baosteel Scholarship for Excellent Students, Baosteel Education Foundation. |
2018 | Academic Star for graduate students, Northwestern Polytechnical University. |
2018 | National scholarship for doctoral students, Ministry of Education of the People's Republic of China. |
2018 | Outstanding postgraduate student pacesetter, Northwestern Polytechnical University |
2017 | National scholarship for doctoral students, Ministry of Education of the People's Republic of China. |
2017 | Academic Star for graduate students, Northwestern Polytechnical University |
2017 | Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University under Grant CX201710. (Host) |
2016 | National scholarship for doctoral students, Ministry of Education of the People's Republic of China. |
[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 JCR Q1, Top Journal, IF=7.050, ESI)
[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 JCR Q1, Top Journal, IF=4.370, ESI)
[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 JCR Q1, IF=4.396, ESI)
[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 JCR Q1, Top Journal, 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 JCR Q1, Top Journal, 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 JCR Q1, Top Journal, 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 JCR Q1, 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 JCR Q1, 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 JCR Q2, 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 JCR Q2, IF=1.685, IOP Publishing China Top Cited Author Award)
[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 JCR Q4, 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 JCR Q2, IF=1.685)
[13]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 JCR Q2, 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 JCR Q2, IF=2.796)
[15]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.
[16]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.
[17] 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.
Reviewer of 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.