Xiaomei Jia
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xiaomei Jia has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Shuihai Dou
Department of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, Beijing, China
2 shared publications - Yanlin Liu
Department of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, Beijing, China
2 shared publications - Yanping Du
Beijing Institute of Graphic Communication, Beijing, China
2 shared publications - Zhaohua Wang
Beijing Institute of Graphic Communication
2 shared publications
Latest Publications
- RetractionOpen Access
- 27 March 2024
- Published by Springer Nature in International Journal of Computational Intelligence Systems
- Vol. 17 (1) , 1
Retraction Note: Research on Feature Extraction and Diagnosis Method of Gearbox Vibration Signal Based on VMD and ResNeXt
- 27 March 2024
- Published by Springer Nature in International Journal of Computational Intelligence Systems
- Vol. 17 (1) , 1
RetractionOpen AccessRetraction Note: Research on Feature Extraction and Diagnosis Method of Gearbox Vibration Signal Based on VMD and ResNeXt
- RetractedOpen Access
- 26 July 2023
- Published by Springer Nature in International Journal of Computational Intelligence Systems
- Vol. 16 (1) , 1-16
RETRACTED ARTICLE: Research on Feature Extraction and Diagnosis Method of Gearbox Vibration Signal Based on VMD and ResNeXt
Aiming at the nonlinear and non-stationarity of gearbox fault signals and the confusion among different fault categories, a gear fault diagnosis method combining variational mode decomposition, reconstruction and ResNeXt is proposed in this paper. In this paper, parameter K of VMD is determined according to the changing trend of sample entropy (SE), K modal components are obtained after decomposition, and the effective modal components are extracted and reconstructed according to Pearson autocorrelation coefficient, so as to remove redundant information from the original signal. Then the reconstructed signal is transformed by time–frequency and output two-dimensional time–frequency information, which is used as the input of ResNeXt model to extract the characteristics of different faults. Moreover, the model performance is improved by changing the learning rate decline rate, and a fault diagnosis model with high precision and good stability is established.
- 26 July 2023
- Published by Springer Nature in International Journal of Computational Intelligence Systems
- Vol. 16 (1) , 1-16
RetractedOpen AccessRETRACTED ARTICLE: Research on Feature Extraction and Diagnosis Method of Gearbox Vibration Signal Based on VMD and ResNeXt
Aiming at the nonlinear and non-stationarity of gearbox fault signals and the confusion among different fault categories, a gear fault diagnosis method combining variational mode decomposition, reconstruction and ResNeXt is proposed in this paper. In this paper, parameter K of VMD is determined according to the changing trend of sample entropy (SE), K modal components are obtained after decomposition, and the effective modal components are extracted and reconstructed according to Pearson autocorrelation coefficient, so as to remove redundant information from the original signal. Then the reconstructed signal is transformed by time–frequency and output two-dimensional time–frequency information, which is used as the input of ResNeXt model to extract the characteristics of different faults. Moreover, the model performance is improved by changing the learning rate decline rate, and a fault diagnosis model with high precision and good stability is established.