Abstract: The vibration signal based circuit breaker faults diagnosis has the problem of time-consuming in feature extraction and poor real-time, which makes the method inapplicable to on-line monitoring. We therefore proposed a circuit breaker energy storage state identification method based on fast extraction of interval features. Firstly, the starting point of the energy storage state of the circuit breaker was detected by the kurtosis-wavelet modulus maximum value, and the vibration signals were marked through KS test to indicate the significant difference in the envelope amplitude. Then the signal envelope was extracted and used as the feature vector, and the ReliefF-SFS method was used to reduce the dimensionality of features to obtain the optimal feature subset. Finally, the fuzzy C-means clustering (KFCM) was used to pre-classify the features to obtain the optimal hyperplane with the least risk, and a training model was established with support vector machine (SVM) for state identification. The experimental results show that the proposed state identification method only takes 0.2 s to extract features with reliable recognition accuracy, which has important application value in the field of circuit breaker state monitoring.