A bearing fault diagnosis method without fault data in new working condition combined dynamic model with deep learning. - Kun Xu; Xianguang Kong; Qibin Wang; Shengkang Yang; Naining Huang; Junji Wang (Adv. Eng. Informatics, 2022)

Deep multiple auto-encoder with attention mechanism network: A dynamic domain adaptation method for rotary machine fault diagnosis under different working conditions. - Shengkang Yang; Xianguang Kong; Qibin Wang; Zhongquan Li; Han Cheng; Kun Xu (Knowl. Based Syst., 2022)

The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data. - Han Cheng; Xianguang Kong; Qibin Wang; Hongbo Ma; Shengkang Yang (Reliab. Eng. Syst. Saf., 2022)

Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions. - Han Cheng; Xianguang Kong; Qibin Wang; Hongbo Ma; Shengkang Yang; Gaige Chen (J. Intell. Manuf., 2023)

Adaptive multispace adjustable sparse filtering: A sparse feature learning method for intelligent fault diagnosis of rotating machinery. - Guowei Zhang; Xianguang Kong; Jingli Du; Jinrui Wang; Shengkang Yang; Hongbo Ma (Eng. Appl. Artif. Intell., 2023)

Remaining useful life prediction combined dynamic model with transfer learning under insufficient degradation data. - Han Cheng; Xianguang Kong; Qibin Wang; Hongbo Ma; Shengkang Yang; Kun Xu (Reliab. Eng. Syst. Saf., 2023)

A zero-sample intelligent fault diagnosis method for bearings based on category relationship model. - Qibin Wang; Ni Liu; Junji Wang; Lei Yin; Shengkang Yang; Naining Huang (Eng. Appl. Artif. Intell., 2024)

A domain adaptation method for bearing fault diagnosis using multiple incomplete source data. - Qibin Wang; Yuanbing Xu; Shengkang Yang; Jiantao Chang; Jingang Zhang; Xianguang Kong (J. Intell. Manuf., 2024)