1. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. (1st March 2018) Authors: Shao, Haidong; Jiang, Hongkai; Lin, Ying; Li, Xingqiu Journal: Mechanical systems and signal processing Issue: Volume 102(2018) Page Start: 278 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
2. A reinforcement ensemble deep transfer learning network for rolling bearing fault diagnosis with Multi-source domains. (January 2022) Authors: Li, Xingqiu; Jiang, Hongkai; Xie, Min; Wang, Tongqing; Wang, Ruixin; Wu, Zhenghong Journal: Advanced engineering informatics Issue: Volume 51(2022) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
3. A reinforcement neural architecture search method for rolling bearing fault diagnosis. (15th March 2020) Authors: Wang, Ruixin; Jiang, Hongkai; Li, Xingqiu; Liu, Shaowei Journal: Measurement Issue: Volume 154(2020) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
4. A unified framework incorporating predictive generative denoising autoencoder and deep Coral network for rolling bearing fault diagnosis with unbalanced data. (June 2021) Authors: Li, Xingqiu; Jiang, Hongkai; Liu, Shaowei; Zhang, Jianjun; Xu, Jun Journal: Measurement Issue: Volume 178(2021) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
5. A Wasserstein gradient-penalty generative adversarial network with deep auto-encoder for bearing intelligent fault diagnosis. (9th January 2020) Authors: Xiong, Xiong; Hongkai, Jiang; Li, Xingqiu; Niu, Maogui Journal: Measurement science & technology Issue: Volume 31:Number 4(2020) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
6. An adaptive deep transfer learning method for bearing fault diagnosis. (February 2020) Authors: Wu, Zhenghong; Jiang, Hongkai; Zhao, Ke; Li, Xingqiu Journal: Measurement Issue: Volume 151(2020) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
7. An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm. (August 2020) Authors: Li, Xingqiu; Jiang, Hongkai; Niu, Maogui; Wang, Ruixin Journal: Mechanical systems and signal processing Issue: Volume 142(2020) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
8. An optimal deep sparse autoencoder with gated recurrent unit for rolling bearing fault diagnosis. (22nd October 2019) Authors: Zhao, Ke; Jiang, Hongkai; Li, Xingqiu; Wang, Ruixin Journal: Measurement science & technology Issue: Volume 31:Number 1(2020) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
9. An optimal variational mode decomposition for rolling bearing fault feature extraction. (9th April 2019) Authors: Wei, Dongdong; Jiang, Hongkai; Shao, Haidong; Li, Xingqiu; Lin, Ying Journal: Measurement science & technology Issue: Volume 30:Number 5(2019:May) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
10. Bearing incipient fault feature extraction using adaptive period matching enhanced sparse representation. (1st March 2022) Authors: Yao, Renhe; Jiang, Hongkai; Li, Xingqiu; Cao, Jiping Journal: Mechanical systems and signal processing Issue: Volume 166(2022) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗