PSO Optimized ANN Diagnosis of Early Gear Pitting

Jialin Li, Yongzhi Qu, Liu Hong, David He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Gear pitting diagnosis has always been an important research topic and different diagnostic methods are used. This paper uses artificial neural network (ANN) to diagnose early gear pitting. There are several issues that are inevitable with the application of ANN for diagnosis of early gear pitting: feature extraction, neural network structure optimization, and training stability. This paper proposes a new feature extraction method that uses fast Fourier transform (FFT) to select a number of frequencies in the frequency spectrum and use the frequency amplitudes as the inputs of ANN. The particle swarm optimization (PSO) is used to optimize the initial value of the network to make the training more stable. Furthermore, the performance of the ANN diagnosis under different working conditions are also compared and analyzed in the paper. The proposed method has been validated through the data collected from the gear pitting test experiment. The validation results have shown that the faults diagnosis accuracy could reach 100%, which proves that the proposed method is reasonable.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
EditorsPing Ding, Chuan Li, Shuai Yang, Ping Ding, Rene-Vinicio Sanchez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1043-1048
Number of pages6
ISBN (Electronic)9781538653791
DOIs
StatePublished - Jan 4 2019
Externally publishedYes
Event2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018 - Chongqing, China
Duration: Oct 26 2018Oct 28 2018

Publication series

NameProceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018

Conference

Conference2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
CountryChina
CityChongqing
Period10/26/1810/28/18

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Keywords

  • Artificial neural network
  • FFT
  • Gear pitting
  • PSO algorithm

Cite this

Li, J., Qu, Y., Hong, L., & He, D. (2019). PSO Optimized ANN Diagnosis of Early Gear Pitting. In P. Ding, C. Li, S. Yang, P. Ding, & R-V. Sanchez (Eds.), Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018 (pp. 1043-1048). [8603493] (Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PHM-Chongqing.2018.00185