The following publications are possibly variants of this publication:
- A Novel Study on a Generalized Model Based on Self-Supervised Learning and Sparse Filtering for Intelligent Bearing Fault DiagnosisGuocai Nie, Zhongwei Zhang, Mingyu Shao, Zonghao Jiao, Youjia Li, Lei Li. sensors, 23(4):1858, February 2023. [doi]
- Sparse Sample Train Axle Bearing Fault Diagnosis: A Semi-Supervised Model Based on Prior Knowledge EmbeddingYaxin Li, Suchao Xie, Jiacheng Wang, Jing Zhang, Hongyu Yan. tim, 72:1-11, 2023. [doi]
- A supervised sparsity-based wavelet feature for bearing fault diagnosisCong Wang, Meng Gan, Chang'an Zhu. jim, 30(1):229-239, 2019. [doi]
- A Deep Ensemble Learning Model for Rolling Bearing Fault DiagnosisRuixin Wang, Hongkai Jiang, Zhenning Li, Yunpeng Liu. icphm 2022: 133-136 [doi]
- Adaptive Robust Noise Modeling of Sparse Representation for Bearing Fault DiagnosisBotao An, Shibin Wang, Ruqiang Yan, Weihua Li 0004, XueFeng Chen. tim, 70:1-12, 2021. [doi]
- Bearing Fault Diagnosis via Stepwise Sparse Regularization with an Adaptive Sparse DictionaryLichao Yu, Chenglong Wang, Fanghong Zhang, Huageng Luo. sensors, 24(8):2445, April 2024. [doi]
- Generalized Gaussian Noise Distribution Enabled Sparse Representation Model for Bearing Fault DiagnosisBotao An, Shibin Wang, Ruqiang Yan, Weihua Li, XueFeng Chen. i2mtc 2020: 1-5 [doi]
- Multiple Enhanced Sparse Representation via IACMDSR Model for Bearing Compound Fault DiagnosisLong Zhang, Lijuan Zhao, Chaobing Wang, Qian Xiao, Haoyang Liu, Hao Zhang, Yanqing Hu. sensors, 22(17):6330, 2022. [doi]