Mechanical Fault Diagnosis of Circuit Breakers Based on XGBoost and Time-domain Features

Abstract

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.

Time-Frequency Fusion Features-Based GSWOA-KELM Model for Gear Fault Diagnosis

“…In addition, feature extraction is an indispensable step before applying machine learning methods for fault diagnosis. The literature [24] extracted 17 time-domain features from circuit breaker vibration signals and input them into the XGBoost model to implement the diagnosis of the mechanical condition of the circuit breaker. The literature [25] used sparse filtering technology to automatically extract the frequency-domain features of gear vibration signals and input them into the Softmax classifier as feature vectors in order to realize gear fault diagnosis.…”