|
个人信息Personal Information
教授 博士生导师 硕士生导师
任职 : 中国仪器仪表学会光机电技术与系统集成分会第三届理事会理事
性别：男
学历：研究生(博士)毕业
学位：博士生
在职信息：在职
所在单位：控制科学与工程学院
入职时间：2010-07-16
联系方式：jiangmingshun@sdu.edu.cn
电子邮箱：jiangmingshun@sdu.edu.cn
扫描关注
- [1] 姚鹏. Intelligent rolling bearing imbalanced fault diagnosis based on Mel-Frequency Cepstrum Coefficient and Convolutional Neural Networks. Measurement, 112143, 2022.
- [2] 王浩淼. Cross-domain open-set rolling bearing fault diagnosis based on feature improvement adversarial network under noise condition. Journal of Intelligent & Fuzzy Systems, 46, 5073, 2024.
- [3] 叶呈龙. Novel cross-domain fault diagnosis method based on model-agnostic meta-learning embedded in adaptive threshold network. Measurement, 113677, 2023.
- [4] 韩同卓. Novel adaptive loss weighted transfer network for partial domain fault diagnosis. ISA Transactions, 362, 2024.
- [5] 张法业. Intelligent rolling bearing compound fault diagnosis based on frequency-domain Gramian angular field and convolutional neural networks with imbalanced data. JOURNAL OF VIBRATION AND CONTROL, 2023.
- [6] 刘繁. Novel short-term low-voltage load forecasting method based on residual stacking frequency attention network. Electric Power Systems Research, 110534, 2024.
- [7] 刘福政. Fault diagnosis of rolling bearing combining improved AWSGMD-CP and ACO-ELM model. Measurement, 112531, 2023.
- [8] 李彦君. Bearing fault diagnosis method based on maximum noise ratio kurtosis product deconvolution with noise conditions. Measurement, 113542, 2023.
- [9] 王金喜. Maximum average impulse energy ratio deconvolution and its application for periodic fault impulses enhancement of rolling bearing. Advanced Engineering Informatics, 101721, 2022.
- [10] 刘福政. Structural discrepancy and domain adversarial fusion network for cross-domain fault diagnosis. ADVANCED ENGINEERING INFORMATICS, 102217, 2023.
- [11] 王浩淼. Multiscale convolutional conditional domain adversarial network with channel attention for unsupervised bearing fault diagnosis. Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, 238, 1123, 2024.
- [12] 刘福政. Balance-blended adversarial distribution and smooth-suppressed labels refinement network for partial transfer fault diagnosis. Engineering Applications of Artificial Intelligence, 135, 2024.
- [13] 刘繁荣. Novel short-term low-voltage load forecasting method based on residual stacking frequency attention network. Electr. Power Syst. Res, 233, 2024.
- [14] 李彦君. Bearing fault diagnosis method based on maximum noise ratio kurtosis product deconvolution with noise conditions. 测量, 221, 2023.
- [15] 王浩淼. Cross-domain open-set rolling bearing fault diagnosis based on feature improvement adversarial network under noise condition. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 46, 5073-5085, 2024.
- [16] 王浩淼. Multiscale convolutional conditional domain adversarial network with channel attention for unsupervised bearing fault diagnosis. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 2024.
- [17] 张法业. Intelligent rolling bearing compound fault diagnosis based on frequency-domain Gramian angular field and convolutional neural networks with imbalanced data. JVC/Journal of Vibration and Control, 2023.
- [18] 王浩淼. Partial transfer learning method based on MDWCAN for rolling bearing fault diagnosis under noisy conditions. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEE, 2024.
- [19] 何佳婕. Fault Diagnosis Method of Rolling Bearing Based on ESGMD-CC and AFSA-ELM. SDHM Structural Durability and Health Monitoring, 18, 37-54, 2024.
- [20] 叶呈龙. Novel cross-domain fault diagnosis method based on model-agnostic meta-learning embedded in adaptive threshold network. 测量, 222, 2023.