Haidong Shao
Haidong Shao

Haidong Shao

Postdoctoral position
Luleå University of Technology
Operation and Maintenance
Operation, Maintenance and Acoustics
Department of Civil, Environmental and Natural Resources Engineering
haidong.shao@ltu.se
+46 (0)920 491559
F103 Luleå

Research Areas

Health Monitoring and Fault Diagnosis, Information Fusion and Intelligent Prognosis

 

Research Activities

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.

 

Publications

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 Q1, Top, IF=5.005, ESI Hot Paper)

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 Q1, Top, IF=7.503, ESI Highly Cited Paper)

[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 Q1, Top, IF=5.005, ESI Highly Cited Paper)

[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 Q2, IF=5.101, ESI Highly Cited Paper)

[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 Q1, Top, IF=5.005, ESI Highly Cited Paper)

[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 Q1, Top, IF=5.005, ESI Highly Cited Paper)

[6] 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 Q2, IF=1.861, ESI Highly Cited Paper)

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 Q2, IF=1.861, IOP Publishing Highly Cited Paper)

Journal 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. 

[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. 

[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. 

[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. 

[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. 

[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. 

[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. 

[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. 

[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. 

[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. 

[11]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. 

[12]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. 

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. 

[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] 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. 

[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. 

Publications

Article in journal

Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing (2019)

Haidong. S, Junsheng. C, Hongkai. J, Yu. Y, Zhantao. W
Knowledge-Based Systems