An active learning method based on uncertainty and complexity for gearbox fault diagnosis

Jiayu Chen, Dong Zhou, Ziyue Guo, Jing Lin, Chuan Lyu, Chen Lu

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


It is crucial to implement an effective and accurate fault diagnosis of a gearbox for mechanical systems. However, being composed of many mechanical parts, a gearbox has a variety of failure modes resulting in the difficulty of accurate fault diagnosis. Moreover, it is easy to obtain raw vibration signals from real gearbox applications, but it requires significant costs to label them, especially for multi-fault modes. These issues challenge the traditional supervised learning methods of fault diagnosis. To solve these problems, we develop an active learning strategy based on uncertainty and complexity. Therefore, a new diagnostic method for a gearbox is proposed based on the present active learning, empirical mode decomposition-singular value decomposition (EMD-SVD) and random forests (RF). First, the EMD-SVD is used to obtain feature vectors from raw signals. Second, the proposed active learning scheme selects the most valuable unlabeled samples, which are then labeled and added to the training data set. Finally, the RF, trained by the new training data, is employed to recognize the fault modes of a gearbox. Two cases are studied based on experimental gearbox fault diagnostic data, and a supervised learning method, as well as other active learning methods, are compared. The results show that the proposed method outperforms the two common types of methods, thus validating its effectiveness and superiority.

Original languageEnglish
Article number8601196
Pages (from-to)9022-9031
Number of pages10
JournalIEEE Access
StatePublished - 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Active learning
  • gearbox fault diagnosis
  • supervised learning
  • uncertainty and complexity


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