Data Segmentation and Augmentation Methods Based on Raw Data Using Deep Neural Networks Approach for Rotating Machinery Fault Diagnosis. - Zong Meng; Xiaolin Guo; Zuozhou Pan; Dengyun Sun; Shuang Liu (IEEE Access, 2019)

2MNet: Multi-sensor and multi-scale model toward accurate fault diagnosis of rolling bearing. - Yang Guan; Zong Meng; Dengyun Sun; Jingbo Liu; Fengjie Fan (Reliab. Eng. Syst. Saf., 2021)

Match-Extracting Chirplet Transform With Application to Bearing Fault Diagnosis. - Quansheng Xu; Jingbo Liu; Yang Guan; Dengyun Sun; Zong Meng (IEEE Trans. Instrum. Meas., 2021)

Research on fault diagnosis method of MS-CNN rolling bearing based on local central moment discrepancy. - Zong Meng; Wei Cao; Dengyun Sun; Qian Li; Wuxu Ma; Fengjie Fan (Adv. Eng. Informatics, 2022)

Intelligent fault diagnosis scheme for rolling bearing based on domain adaptation in one dimensional feature matching. - Dengyun Sun; Zong Meng; Yang Guan; Jingbo Liu; Wei Cao; Fengjie Fan (Appl. Soft Comput., 2023)

A novel diagnostic framework based on vibration image encoding and multi-scale neural network. - Yang Guan; Zong Meng; Jimeng Li; Wei Cao; Dengyun Sun; Jingbo Liu; Fengjie Fan (Expert Syst. Appl., 2024)

A novel sliding mixing graph contrastive domain adaptation method for fault diagnosis under time-varying speeds. - Kai Chen; Zong Meng; Dengyun Sun; Wei Cao; Yonglei Ren; Weiliang Sun; Jie Wu (Expert Syst. Appl., 2025)