Gear pitting fault diagnosis using integrated CNN and GRU network with both vibration and acoustic emission signals

Xueyi Li, Jialin Li, Yongzhi Qu, David He

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

This paper deals with gear pitting fault diagnosis problem and presents a method by integrating convolutional neural network (CNN) and gated recurrent unit (GRU) networks with vibration and acoustic emission signals to solve the problem. The presented method first trains a one-dimensional CNN with acoustic emission signals and a GRU network with vibration signals. Then the gear pitting fault features obtained by the two networks are concatenated to form a deep learning structure for gear pitting fault diagnosis. Seven different gear pitting conditions are used to test the feasibility of the presented method. The diagnosis result of the gear pitting fault shows that the accuracy of the presented method reaches above 98% with only a relatively small number of training samples. In comparison with the results using CNN or GRU network alone, the presented method gives more accurate diagnosis results. By comparing the results of different loads and learning rates, the robustness of the presented method for gear pitting fault diagnosis is proved. Moreover, the presented deep structure can be easily extended to more other sensor input signals for gear pitting fault diagnosis in the future.

Original languageEnglish (US)
Article number768
JournalApplied Sciences (Switzerland)
Volume9
Issue number4
DOIs
StatePublished - Feb 22 2019
Externally publishedYes

Keywords

  • Acoustic emission signal
  • Gated recurrent unit
  • Gear pitting fault diagnosis
  • One-dimensional convolutional neural network
  • Vibration signal

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