Imbalanced fault diagnosis based on semi-supervised ensemble learning

Experimental results show that the performance of proposed method exceeds that of other methods, which is very close to the upper bound of fully-supervised performance, which substantially indicates that this study provides a very promising methodology for imbalanced fault diagnosis.