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 contribution | Diagnosis of Gear Early Pitting Faults Using PSO Optimized Deep Neural Network |
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Original language | Chinese (Traditional) |
Pages (from-to) | 974-979 |
Number of pages | 6 |
Journal | Dongbei Daxue Xuebao/Journal of Northeastern University |
Volume | 40 |
Issue number | 7 |
DOIs | |
State | Published - Jul 1 2019 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2019, Editorial Department of Journal of Northeastern University. All right reserved.
Keywords
- Deep neural network(DNN)
- Early pitting
- Gear
- PSO algorithm
- Principal component analysis(PCA)