Modeling New Nature of Extraction and State Identification of Vibration Shock Signals From Hydroelectric Generating Units Using LCGSA Optimized RBF Combined With CEEMDAN Sample Entropy

Modeling New Nature of Extraction and State Identification of Vibration Shock Signals From Hydroelectric Generating Units Using LCGSA Optimized RBF Combined With CEEMDAN Sample Entropy

The feature extraction and state recognition of vibration signals of hydroelectric generating units are of great significance for effectively ensuring the safety and lifespan of unit operation. This paper introduces a novel fault diagnosis approach that leverages a wavelet threshold algorithm for in...

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Bibliographic Details
Main Authors: Xiang Li, Yun Zeng, Jing Qian, Boyi Xiao, Neng Fei, Fang Dao, Yidong Zou
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
CEEMDAN
sample entropy
LCGSA-RBF
vibration shock signals
hydroelectric generating units
Online Access:https://ieeexplore.ieee.org/document/10542994/
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