Open Access
| Issue |
Res. Des. Nucl. Eng.
Volume 2, 2026
|
|
|---|---|---|
| Article Number | 2025010 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/rdne/2025010 | |
| Published online | 30 January 2026 | |
- Y. Shu, et al., Building a new electric power system based on new energy sources, Strateg. Study Chin. Acad. Eng. 23, 61–69 (2021). [Google Scholar]
- S. Ling, China’s neighborhood security cooperation under the holistic approach to national security, J. Int. Secur. Stud. 42, 28–52, 156, 157 (2024). [Google Scholar]
- G. Chai, et al., Nuclear safety improvement actions and safety requirements in post-Fukushima era, Atom. Energy Sci. Technol. 56, 399–409 (2022). [Google Scholar]
- C. Zhao, Development status and outlook for nuclear power in China, Nucl. Power Eng. 3, 13 (2018). [Google Scholar]
- X. Liang, J. Zuo, Z. Feng, Dynamic modeling of gearbox faults: A review, Mech. Syst. Signal Process. 98, 852–876 (2018). [Google Scholar]
- X. Yang, et al., A new fault diagnosis method for multistage gearbox based on GAPSO-SVM, J. Xi’an Univ. Technol. 38, 519–525 (2022). [Google Scholar]
- A. Amin, et al., A Bayesian deep learning framework for reliable fault diagnosis in wind turbine gearboxes under various operating conditions, Wind Eng. 48, 297–309 (2024). [Google Scholar]
- C. Wang, et al., Weak fault diagnosis of planetary gearbox based on IFMD under time-varying speed, J of Vib. Eng. 37, 1980–1992 (2024). [Google Scholar]
- M. Praveen, et al., Wavelet based real-time planetary gearbox health monitoring under non-stationary operation, Exp. Tech. 46, 861–875 (2021). [Google Scholar]
- D. Calle, et al., A context-aware oil debris-based health indicator for wind turbine gearbox condition monitoring, Energies. 12, 33–73 (2019). [Google Scholar]
- M. Soualhi, et al., Health monitoring of bearing and gear faults by using a new health indicator extracted from current signals, Measurement 141, 37–51 (2019). [Google Scholar]
- M. ThanhTung, et al., Effect of phase fluctuation on the proper operation of smart gear health monitoring system, Sensors 22, 3231–3231 (2022). [Google Scholar]
- I. Vamsi, et al., An integrated condition monitoring scheme for health state identification of a multi-stage gearbox through Hurst exponent estimates, Struct. Health Mon. 22, 730–745 (2023). [Google Scholar]
- Z. Wan, et al., Gearbox fault diagnosis method based on optimized VMD-NLM with 1DDRSN, J. Mech. Transm. 49, 150–160 (2025). [Google Scholar]
- A. Adel, et al., Gearbox fault diagnosis using REMD, EO and machine learning classifiers, J. Vib. Eng. Technol. 12, 4673–4697 (2023). [Google Scholar]
- A. Abdelrahman, et al., Vibration based fault diagnostics in a wind turbine planetary gearbox using machine learning, Wind Eng. 47, 175–189 (2023). [Google Scholar]
- K. Shukla, et al., Enhancing gearbox fault diagnosis through advanced feature engineering and data segmentation techniques, Machines. 12, 261 (2024). [Google Scholar]
- J. Ding, D. Xiao, X. Li, Gear Fault Diagnosis based on genetic mutation particle swarm optimization VMD and probabilistic neural network algorithm, IEEE Access. 8, 18456–18474 (2020). [Google Scholar]
- F. Wang, H. Gong, Q. Ni, Non-intrusion gear fault diagnosis of 400 km/h+ high-speed train based on signal fusion of stator current and electromagnetic torque, Int. J. Automot. Technol. 113 (2025). [Google Scholar]
- F. Liu, et al., Spectral denoising random feature decomposition and its application in gear fault diagnosis, Appl. Acoust. 231, 110562 (2025). [Google Scholar]
- P. Goswami, N. Rai, A Novel methodology for chipped teeth and worn tooth gear fault detection using minimum entropy deconvolution and CEEMDAN, J. Vib. Eng. Technol. 13, 11 (2025). [Google Scholar]
- M. Batsch, B. Kiczek, Gear fault detection method based on convex hull clustering of autoencoder’s latent space, Appl. Sci. 14, 5282 (2024). [Google Scholar]
- H. Wang, et al., Fault diagnosis of planetary gearbox using ICEEMDAN and SVM, Mech. Sci. Technol. Aerosp. Eng. 42, 24–30 (2023). [Google Scholar]
- J. Zheng, H. Pan, Mean-optimized mode decomposition: An improved EMD approach for non-stationary signal processing, ISA Trans. 106, 392–401 (2020). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.