SSPENet: Semi-supervised prototype enhancement network for rolling bearing fault diagnosis under limited labeled samples. - Xuejian Yao; Xingchi Lu; Quansheng Jiang; Yehu Shen; Fengyu Xu 001; Qixin Zhu (Adv. Eng. Informatics, 2024)

Self-supervised learning-based dual-classifier domain adaptation model for rolling bearings cross-domain fault diagnosis. - Quansheng Jiang; Xiaoshan Lin; Xingchi Lu; Yehu Shen; Qixin Zhu; Qingkui Zhang (Knowl. Based Syst., 2024)

Enhanced residual convolutional domain adaptation network with CBAM for RUL prediction of cross-machine rolling bearing. - Xingchi Lu; Quansheng Jiang; Yehu Shen; Xiaoshan Lin; Fengyu Xu 001; Qixin Zhu (Reliab. Eng. Syst. Saf., 2024)

Semi-Supervised Contrastive Domain Adaptation Network for Fault Diagnosis of Rotating Machinery Under Cross-Working Conditions. - Xingchi Lu; Liuyang Song; Changkun Han; Quansheng Jiang; Weiyang Xu; Huaqing Wang (IEEE Internet Things J., 2025)