A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

Jialin Li, Xueyi Li, David He, Yongzhi Qu

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

In recent years, research on gear pitting fault diagnosis has been conducted. Most of the research has focused on feature extraction and feature selection process, and diagnostic models are only suitable for one working condition. To diagnose early gear pitting faults under multiple working conditions, this article proposes to develop a domain adaptation diagnostic model–based improved deep neural network and transfer learning with raw vibration signals. A particle swarm optimization algorithm and L2 regularization are used to optimize the improved deep neural network to improve the stability and accuracy of the diagnosis. When using the domain adaptation diagnostic model for fault diagnosis, it is necessary to discriminate whether the target domain (test data) is the same as the source domain (training data). If the target domain and the source domain are consistent, the trained improved deep neural network can be used directly for diagnosis. Otherwise, the transfer learning is combined with improved deep neural network to develop a deep transfer learning network to improve the domain adaptability of the diagnostic model. Vibration signals for seven gear types with early pitting faults under 25 working conditions collected from a gear test rig are used to validate the proposed method. It is confirmed by the validation results that the developed domain adaptation diagnostic model has a significant improvement in the adaptability of multiple working conditions.

Original languageEnglish (US)
Pages (from-to)168-182
Number of pages15
JournalProceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Volume234
Issue number1
DOIs
StatePublished - Feb 1 2020
Externally publishedYes

Keywords

  • Early gear pitting
  • improved deep neural network
  • multiple working conditions
  • transfer learning

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