Research output

Research output

[1]      X. Chen, R. Yang, Y. Xue, B. Song, and Z. Wang, “TFPred: Learning Discriminative Representations from Unlabeled Data for Few-Label Rotating Machinery Fault Diagnosis,” Control Engineering Practice, 2024.

[2]      Y. Xue, R. Yang, X. Chen, Z. Tian, and Z. Wang, “A Novel Local Binary Temporal Convolutional Neural Network for Bearing Fault Diagnosis,” IEEE Transactions on Instrumentation and Measurement, 2023.

[3]      X. Chen, R. Yang, Y. Xue, M. Huang, R. Ferrero, and Z. Wang, “Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016,” IEEE Transactions on Instrumentation and Measurement, 2023.

[4]      Z. Liu, R. Yang, W. Liu, and X. Liu, “IFRN: Insensitive Feature Removal Network for Zero-shot Mechanical Fault Diagnosis across Fault Severity,” Neurocomputing, 2023.

[5]      X. Chen, R. Yang, C. Yang, B. Song, and M. Zhong, “A Novel Momentum Prototypical Neural Network to Cross-Domain Fault Diagnosis for Rotating Machinery Subject to Cold-Start,” Neurocomputing, 2023.

[6]      Y. Li, R. Yang, and H. Wang, “Unsupervised Method Based on Adversarial Domain Adaption for Bearing Fault Diagnosis,” Applied Sciences, 2023.