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 language||English (US)|
|Title of host publication||Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018|
|Editors||Ping Ding, Chuan Li, Shuai Yang, Ping Ding, Rene-Vinicio Sanchez|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Jan 4 2019|
|Event||2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018 - Chongqing, China|
Duration: Oct 26 2018 → Oct 28 2018
|Name||Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018|
|Conference||2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018|
|Period||10/26/18 → 10/28/18|
Bibliographical noteFunding Information:
ACKNOWLEDGEMENT Sponsor: NSFC grant no. 51675089.
© 2018 IEEE.
- Artificial neural network
- Gear pitting
- PSO algorithm