PSO优化深度神经网络诊断齿轮早期点蚀故障

Translated title of the contribution: Diagnosis of Gear Early Pitting Faults Using PSO Optimized Deep Neural Network

Jia Lin Li, David He, Yong Zhi Qu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

When gear faults were diagnosed based on the data-driven method, feature extraction was generally performed by Fourier transform, etc. The feature extraction method used has a great influence on the diagnosis results. Therefore, deep neural network(DNN) was proposed to diagnose early gear pitting faults and the vibration signals are directly used as the network inputs to avoid errors caused by feature extraction. In addition, the particle swarm optimization(PSO) algorithm was applied to optimize the DNN for obtaining a more stable training process and better diagnosis results. Principal component analysis(PCA) algorithm was used to reduce the dimensions of the DNN outputs. The data collected from the experiment was used to train and test the DNN. The fault diagnostic accuracy can reach over 90%, which proves that the proposed method is reasonably effective.

Translated title of the contributionDiagnosis of Gear Early Pitting Faults Using PSO Optimized Deep Neural Network
Original languageChinese
Pages (from-to)974-979
Number of pages6
JournalDongbei Daxue Xuebao/Journal of Northeastern University
Volume40
Issue number7
DOIs
StatePublished - Jul 1 2019
Externally publishedYes

Keywords

  • Deep neural network(DNN)
  • Early pitting
  • Gear
  • PSO algorithm
  • Principal component analysis(PCA)

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