Journal of Northeastern University Natural Science ›› 2019, Vol. 40 ›› Issue (7): 974-979.DOI: 10.12068/j.issn.1005-3026.2019.07.012

• Mechanical Engineering • Previous Articles     Next Articles

Diagnosis of Gear Early Pitting Faults Using PSO Optimized Deep Neural Network

LI Jia-lin1, HE David1,2, QU Yong-zhi3   

  1. 1. School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China; 2. Department of Mechanical & Industrial Engineering, University of Illinois at Chicago, Chicago 60607, USA; 3. School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China.
  • Received:2017-04-29 Revised:2017-04-29 Online:2019-07-15 Published:2019-07-16
  • Contact: HE David
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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.

Key words: gear, early pitting, PSO algorithm, deep neural network(DNN), principal component analysis(PCA)

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