1. A novel performance degradation prognostics approach and its application on ball screw. (31st May 2022) Authors: Zhang, Xiaochen; Luo, Tianjian; Han, Te; Gao, Hongli Journal: Measurement Issue: Volume 195(2022) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
2. A physically consistent framework for fatigue life prediction using probabilistic physics-informed neural network. (January 2023) Authors: Zhou, Taotao; Jiang, Shan; Han, Te; Zhu, Shun-Peng; Cai, Yinan Journal: International journal of fatigue Issue: Volume 166(2023) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
3. A planetary gearbox fault diagnosis method based on time-series imaging feature fusion and a transformer model. (1st February 2023) Authors: Wu, Rui; Liu, Chao; Han, Te; Yao, Jiachi; Jiang, Dongxiang Journal: Measurement science & technology Issue: Volume 34:Number 2(2023) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
4. An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems. (15th February 2019) Authors: Han, Te; Liu, Chao; Wu, Linjiang; Sarkar, Soumik; Jiang, Dongxiang Journal: Mechanical systems and signal processing Issue: Volume 117(2019) Page Start: 170 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
5. An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications. (January 2023) Authors: Zhou, Taotao; Zhang, Laibin; Han, Te; Droguett, Enrique Lopez; Mosleh, Ali; Chan, Felix T.S. Journal: Reliability engineering & system safety Issue: Volume 229(2023) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
6. Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. (May 2018) Authors: Han, Te; Jiang, Dongxiang; Zhao, Qi; Wang, Lei; Yin, Kai Journal: Transactions of the Institute of Measurement and Control Issue: Volume 40:Number 8(2018) Page Start: 2681 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
7. Cross‐machine intelligent fault diagnosis of gearbox based on deep learning and parameter transfer. Issue 3 (11th December 2021) Authors: Han, Te; Zhou, Taotao; Xiang, Yongyong; Jiang, Dongxiang Journal: Structural control and health monitoring Issue: Volume 29:Issue 3(2022) Page Start: n/a Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
8. Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data. (15th May 2023) Authors: Yao, Jiachi; Han, Te Journal: Energy Issue: Volume 271(2023) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
9. Deep network-based maximum correlated kurtosis deconvolution: A novel deep deconvolution for bearing fault diagnosis. (15th April 2023) Authors: Miao, Yonghao; Li, Chenhui; Shi, Huifang; Han, Te Journal: Mechanical systems and signal processing Issue: Volume 189(2023) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
10. Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application. (February 2020) Authors: Han, Te; Liu, Chao; Yang, Wenguang; Jiang, Dongxiang Journal: ISA transactions Issue: Volume 97(2020) Page Start: 269 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗