The spring operating mechanism provides energy for the high voltage circuit breaker opening and closing

and its reliability is directly related to the normal operation of the circuit breaker.Therefore

it is of great significance to perform state detection on the circuit breaker spring operating mechanism.IEPE piezoelectric vibration sensor is adopted to analyzing strong vibration signals accompanying the circuit breaker body during spring storage and instantaneous release.Then a spring state detection method for high voltage circuit breaker utilizing energy feature extraction algorithm of local mean decomposition（LMD） combined with support vector machine（SVM） theory is proposed.Vibration signals are decomposed by Local Mean Decomposition to obtain a series of Product Function（PF）components.The normalized energy of each PF component containing the main spring abnormality information is calculated and used as a feature vector to input support vector machine.The parameters of SVM are optimized by grid parameter optimization method.The experimental analysis shows that the identification accuracy of this method is up to 97.5%

which can effectively identify the fault type caused by abnormal spring energy status of circuit breaker operating mechanism.