Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

Xueyi LI, Jialin LI, Yongzhi QU, David HE

Research output: Contribution to journalArticlepeer-review

71 Scopus citations


In aerospace industry, gears are the most common parts of a mechanical transmission system. Gear pitting faults could cause the transmission system to crash and give rise to safety disaster. It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder (ADSAE) is proposed. The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data. This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear. The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions. The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy. This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults. The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods. This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.

Original languageEnglish (US)
Pages (from-to)418-426
Number of pages9
JournalChinese Journal of Aeronautics
Issue number2
StatePublished - Feb 2020

Bibliographical note

Funding Information:
This study was supported by the Natural Science Foundation of China (No. 51675089 ).

Publisher Copyright:
© 2019 Chinese Society of Aeronautics and Astronautics


  • Deep learning
  • Gear pitting diagnosis
  • Gear teeth
  • Raw vibration signal
  • Semi-supervised learning
  • Sparse autoencoder


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