Feature importance analysis for power circuit breaker vibration-based condition assessment

This paper provides an overview of the influential parameters for the power circuit breaker condition assessment based on the vibration fingerprint. By creating the feature subsets based on the domain of computation originating from the vibration fingerprint, the features are firstly ranked by four features ranking algorithms. To confirm the ranked feature contribution to the classification performance, 11 different machine learning classifiers are trained. The training of the classifier is performed on the complete feature set where afterward the same classifiers are trained with the subset of the features ordered by the ranking algorithms. The ranking and the classifier performance yield the concept of kurtosis in the time and frequency domain as a highly promising feature for binary classification which credibly reflects the circuit breaker's mechanical condition.