Abstract: The accurate fault pattern identification for the high voltage circuit breaker (HVCB) plays an important role in the development of mine smart grids. Aiming at the inaccessible obtainment of fault data and the lack of fault sam- ples,a method of fault recognition was proposed based on the incremental learning algorithm for SVM. Firstly,the state monitoring variables were determined by the current signal and voltage signal of control unit and the vibration signal of the switching for HVCB. Secondly,four common faults,including the spring loosening,the core jamming,the coil aging and the abnormal electrical power supply,were simulated. Then the fault features were extracted,and the fault data samples as well as the incremental learning data samples were established. After training fault data samples based on the incremental learning algorithm for SVM,the fault recognition model was acquired and its accuracy was validated through exerting the new fault data samples into the model. Finally,it is shown that the incremental learning algorithm for SVM can be used to recognize the above four common faults for HVCB effectively,and its recognition accuracy can be improved by continuous learning on new samples.