| Chen, Chong, Fu, Huibin, Zheng, Yu, Tao, Fei and Liu, Ying  ORCID: https://orcid.org/0000-0001-9319-5940
      2023.
      
      The advance of digital twin for predictive maintenance:  The role and function of machine learning.
      Journal of Manufacturing Systems
      71
      
      , pp. 581-594.
      
      10.1016/j.jmsy.2023.10.010   | 
|  | 
| Chen, Chong, Wu, Dazhong and Liu, Ying  ORCID: https://orcid.org/0000-0001-9319-5940
      2022.
      
      Recent advances of AI for engineering service and maintenance.
      Autonomous Intelligent Systems
      2
      
        (1)
      
      
      , 19.
      10.1007/s43684-022-00038-y   | 
|   | 
| You, Yingchao, Chen, Chong, Hu, Fu, Liu, Ying  ORCID: https://orcid.org/0000-0001-9319-5940 and Ji, Ze  ORCID: https://orcid.org/0000-0002-8968-9902
      2022.
      
      Advances of digital twins for predictive maintenance.
      Presented at: 3rd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2021),
      Linz, Austria,
      17-19 November 2021.
      
      
      
      
      
       , vol.200
      
      Procedia Computer Science, Vol 200: 
      
      pp. 1471-1480.
      10.1016/j.procs.2022.01.348 | 
|  | 
| Chen, Chong, Liu, Ying  ORCID: https://orcid.org/0000-0001-9319-5940, Sun, Xianfang, Cairano-Gilfedder, Carla Di and Titmus, Scott
      2021.
      
      An integrated deep learning-based approach for automobile maintenance prediction with GIS data.
      Reliability Engineering and System Safety
      216
      
      
      , 107919.
      10.1016/j.ress.2021.107919 | 
|   | 
| Chen, Chong, Liu, Ying  ORCID: https://orcid.org/0000-0001-9319-5940, Sun, Xianfang  ORCID: https://orcid.org/0000-0002-6114-0766, Di Cairano-Gilfedder, Carla and Titmus, Scott
      2020.
      
      Automobile maintenance modelling using gcForest.
      Presented at: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE),
      Virtual,
      20-24 August 2020.
      
      2020 IEEE 16th International Conference on Automation Science and Engineering (CASE).
      
      
      
       
      
      
      IEEE,
      pp. 600-605.
      10.1109/CASE48305.2020.9216745   | 
|  | 
| Chen, Chong
      2020.
      Deep learning for automobile predictive maintenance under Industry 4.0.
      PhD Thesis,
      Cardiff University.   Item availability restricted. | 
|    | 
| Chen, Chong, Liu, Ying  ORCID: https://orcid.org/0000-0001-9319-5940, Wang, Shixuan, Di Cairano-Gilfedder, Carla, Sun, Xianfang  ORCID: https://orcid.org/0000-0002-6114-0766, Titmus, Scott and Syntetos, Aris A.  ORCID: https://orcid.org/0000-0003-4639-0756
      2020.
      
      Predictive maintenance using cox proportional hazard deep learning.
      Advanced Engineering Informatics
      44
      
      
      , 101054.
      10.1016/j.aei.2020.101054 | 
|  | 
| Chen, Chong, Liu, Ying  ORCID: https://orcid.org/0000-0001-9319-5940, Kumar, Maneesh  ORCID: https://orcid.org/0000-0002-2469-1382, Qin, Jian and Ren, Yunxia
      2019.
      
      Energy consumption modelling using deep learning embedded semi-supervised learning.
      Computers and Industrial Engineering
      135
      
      , pp. 757-765.
      
      10.1016/j.cie.2019.06.052 | 
|  | 
| Chen, Chong, Liu, Ying  ORCID: https://orcid.org/0000-0001-9319-5940, Sun, Xianfang  ORCID: https://orcid.org/0000-0002-6114-0766, Cairano-Gilfedder, Carla and Titmus, Scott
      2019.
      
      Automobile maintenance prediction using deep learning with GIS data.
      Procedia CIRP
      81
      
      , -.
      
      10.1016/j.procir.2019.03.077 | 
|  | 
| Chen, Chong, Liu, Ying  ORCID: https://orcid.org/0000-0001-9319-5940, Kumar, Maneesh  ORCID: https://orcid.org/0000-0002-2469-1382 and Qin, Jian
      2018.
      
      Energy consumption modelling using deep learning technique — a case study of EAF.
      Procedia CIRP
      72
      
      , pp. 1063-1068.
      
      10.1016/j.procir.2018.03.095 | 
|  | 
| Chen, Chong, Liu, Ying  ORCID: https://orcid.org/0000-0001-9319-5940, Sun, Xianfang  ORCID: https://orcid.org/0000-0002-6114-0766, Wang, Shixuan, Di Cairano-Gilfedder, Carla, Titmus, Scott and Syntetos, Aris  ORCID: https://orcid.org/0000-0003-4639-0756
      2018.
      
      Reliability analysis using deep learning.
      Presented at: ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference,
      Quebec City, Canada,
      26-29 August 2018.
      
      ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Volume 1B: 38th Computers and Information in Engineering Conference Quebec City, Quebec, Canada, August 26–29, 2018.
      
      
      
       
      
      
      ASME,
      V01BT02A040.
      10.1115/DETC2018-86172 | 
|   | 

 
							

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