A Fault Diagnosis Method of Rotating Machinery Based on One-Dimensional, Self-Normalizing Convolutional Neural Networks. - Jingli Yang; Shuangyan Yin; Yongqi Chang; Tianyu Gao 002 (Sensors, 2020)

An Efficient Method for Monitoring Degradation and Predicting the Remaining Useful Life of Mechanical Rotating Components. - Jingli Yang; Shuangyan Yin; Yongqi Chang; Tianyu Gao 002; Cheng Yang (IEEE Trans. Instrum. Meas., 2021)

A novel convolutional neural network with interference suppression for the fault diagnosis of mechanical rotating components. - Jingli Yang; Shuangyan Yin; Chao Sun; Tianyu Gao 002 (Neural Comput. Appl., 2022)

A shapelet-driven distillation generation method for generalized zero-shot learning in compound fault diagnosis. - Shuangyan Yin; Jingli Yang; Yongqi Chang; Ye Li; Changdong Wang 002 (Neurocomputing, 2025)

A Zero-Shot Framework for Compound Fault Diagnosis in Rotating Machinery Using Orthogonal High-Order Semantics and Cross-Hierarchical Discriminative Learning. - Shuangyan Yin; Jingli Yang; Yongqi Chang; Changdong Wang 002; Tianyu Gao 002; Kaijie Wang (IEEE Trans. Instrum. Meas., 2025)