To solve the imbalance problem of wind turbine dataset, a Borderline Synthetic Minority Oversampling Technique-Sequence (BSMOTE-Sequence) sampling algorithm was proposed. In the algorithm, when synthesizing new samples, the space and time characteristics were considered comprehensively, and the new samples were cleaned, so as to effectively reduce the generation of noise points. Firstly, the minority class samples were divided into security class samples, boundary class samples and noise class samples according to the class proportion of the nearest neighbor samples of each minority class sample. Secondly, for each boundary class sample, the minority class sample set with the closest spatial distance and time span was selected, the new samples were synthesized by linear interpolation method, and the noise class samples and the overlapping samples between classes were filtered out. Finally, Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) were used as the fault detection models of wind turbine gear box, and F1-Score, Area Under Curve (AUC) and G-mean were used as performance evaluation indices of the models, and the proposed algorithm was compared with other sampling algorithms on real wind turbine datasets. Experimental results show that, compared with those of the existing algorithms, the classification effect of the samples generated by BSMOTE-Sequence algorithm is better with an average increase of 3% in F1-Score, AUC and G-mean of the detection models. The proposed algorithm can be effectively applicable to the field of wind turbine fault detection where the data with sequential rule is imbalanced.