Cargando…
A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy
Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition wi...
Autores principales: | Shang, Haikun, Xu, Junyan, Li, Yucai, Lin, Wei, Wang, Jinjuan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534344/ https://www.ncbi.nlm.nih.gov/pubmed/34682043 http://dx.doi.org/10.3390/e23101319 |
Ejemplares similares
-
Optimized Ship-Radiated Noise Feature Extraction Approaches Based on CEEMDAN and Slope Entropy
por: Li, Yuxing, et al.
Publicado: (2022) -
Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine
por: Shang, Haikun, et al.
Publicado: (2019) -
Denoising of blasting vibration signals based on CEEMDAN-ICA algorithm
por: Wenjun, Bai, et al.
Publicado: (2023) -
A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy
por: Shang, Haikun, et al.
Publicado: (2020) -
Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS
por: Kuai, Moshen, et al.
Publicado: (2018)