Wentao Mao

TL;DR: A new RUL prediction method based on deep feature representation and transfer learning is proposed in this paper, which shows a significant performance improvement when using the PHM Challenging 2012 data set.

TL;DR: An online sequential prediction method for imbalanced fault diagnosis problem is proposed based on extreme learning machine and proves that, even existing information loss, the proposed method has lower bound of the model reliability.

TL;DR: The results show that the proposed method can provide a better solution for imbalanced fault diagnosis on the basis of generating similar fault samples and outperforms three widely used sample synthesis techniques, such as random oversampling, synthetic minority oversamplings technique, and the principal curve-based oversampler method in terms of diagnosis accuracy and numerical stability.

TL;DR: A new deep auto-encoder method with fusing discriminant information about multiple fault types is proposed for bearing fault diagnosis that can effectively improve the diagnostic accuracy with acceptable time efficiency and the results on the Kruskal–Wallis Test indicate the proposed method has better numerical stability.

TL;DR: Experimental results show that the proposed deep output kernel learning method can effectively improve the accuracy of bearing fault diagnosis in an acceptable time and the results from the Kruskal-Wallis Test indicate the proposed method has good numerical stability.