The invention relates to a breaker fault diagnosis method based on separating/closing coil current signals, which includes the following steps: 1, collecting a primary fault dataset and performing normalization processing to the collected data; 2, adopting the algorithm P-KFCM combined by the algorithm PSO (particle swarm optimization) and the algorithm KFCM (kernel fuzzy C-mean clustering) to classify the data set, so as to obtain a primary fault sample membership matrix and classifying the dataset into C types according to the degree of membership; 3, verifying the cluster number C through the cluster effectiveness index MPC, and setting the cluster number C corresponding to the maximum value of MPC as the optimal cluster number; 4, building an SVM predicating trainer through data samples of every two types in the optimal classification, so as to obtain C*(C-1)/2 SVM training models; 5, inputting the collected test samples in the SVM training models, and obtaining the primary testing results; 6, obtaining the final fault diagnosis by combining all the testing results. Through the adoption of the method, the accuracy of fault classification can be improved, and the on-line detection on fault types of breakers can be realized.