Open AccessMechanical Fault Diagnosis of Circuit Breakers Based on XGBoost and Time-domain Features
Author(s) -
Jiajin Qi,
Qingkui He,
Yijun Jiang,
Yinfei Xu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1616/1/012105
Subject(s) - circuit breaker , feature extraction , fault (geology) , vibration , time domain , feature (linguistics) , computer science , signal (programming language) , engineering , pattern recognition (psychology) , reliability (semiconductor) , artificial intelligence , acoustics , electrical engineering , physics , computer vision , power (physics) , linguistics , philosophy , quantum mechanics , seismology , geology , programming language
In order to improve the efficiency of feature extraction of mechanical vibration signal of circuit breaker and the reliability of state recognition of circuit breakers, a mechanical fault diagnosis method of high voltage circuit breaker based on XGBoost is adopted. Firstly, 17 time-domain features are extracted from the measured vibration signals of circuit breakers, constructing feature vector and the separability of eigenvectors is analyzed. Then the feature vector is input into XGBoost, the depth and size of the tree are optimized to realize the high reliability discriminant analysis of the mechanical state of circuit breaker. Experiments on vibration data of circuit breakers prove that, this method has high efficiency in feature extraction and the overall recognition accuracy is high.