Detection of pitting in gears using a deep sparse autoencoder

Yongzhi Qu, Miao He, Jason Deutsch, David He

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

In this paper a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised machine learning technique. A stacked autoencoder network with multiple hidden layers is considered to be a deep learn ing network. The presented method uses a stacked autoencoder network to perform the dictionary learning in sparse coding and extract features from raw vibration data automatically. These features are then used to perform gear pitting fault detection. The presented method is validated with vibration data collected from gear tests with pitting faults in a gearbox test rig and compared with an existing deep learning-based approach.

Original languageEnglish (US)
Article number515
JournalApplied Sciences (Switzerland)
Volume7
Issue number5
DOIs
StatePublished - May 16 2017
Externally publishedYes

Bibliographical note

Funding Information:
This work was partially supported by NSFC (51505353) and NSF of Hubei Province (2016CFB584).

Publisher Copyright:
© 2017 by the authors.

Keywords

  • Deep learning
  • Deep sparse autoencoder
  • Gear
  • Pitting detection
  • Vibration

Fingerprint

Dive into the research topics of 'Detection of pitting in gears using a deep sparse autoencoder'. Together they form a unique fingerprint.

Cite this