TY - JOUR
T1 - Deep Convolutional Neural Network Framework for Diagnostics of Planetary Gearboxes Under Dynamic Loading With Feature-Level Data Fusion
AU - Gecgel, Ozhan
AU - Ekwaro-Osire, Stephen
AU - Gulbulak, Utku
AU - Morais, Tobias Souza
N1 - Publisher Copyright:
Copyright © 2021 by ASME.
PY - 2022/6
Y1 - 2022/6
N2 - Planetary gearboxes are susceptible to premature failures due to cyclic random loadings and extreme operating conditions. Fault diagnostics strategies are crucial to increase operational safety and reduce economic costs. This led to the research question is: Can a deep convolutional neural network (DCNN) with data fusion improve diagnostics of a planetary gearbox using simulated data? To answer this question, a DCNN framework was proposed to diagnose planetary gearbox with crack using simulated time and the frequency response. A finite element model was developed to generate a time-varying mesh stiffness response for gear tooth meshing at different crack levels. The mesh stiffness was expanded in terms of the Fourier series to generate values at any rotational speed and time interval. The generated mesh stiffness response was used on a dynamic model to generate the time and frequency response of the system. An additional data set was generated using feature-level data fusion. The two datasets were fed to the DCNN model to diagnose the crack faults and results were compared. It was shown that the feature-level data fusion method is very robust in diagnosing crack faults with good accuracy rates even with the presence of a high level of noise.
AB - Planetary gearboxes are susceptible to premature failures due to cyclic random loadings and extreme operating conditions. Fault diagnostics strategies are crucial to increase operational safety and reduce economic costs. This led to the research question is: Can a deep convolutional neural network (DCNN) with data fusion improve diagnostics of a planetary gearbox using simulated data? To answer this question, a DCNN framework was proposed to diagnose planetary gearbox with crack using simulated time and the frequency response. A finite element model was developed to generate a time-varying mesh stiffness response for gear tooth meshing at different crack levels. The mesh stiffness was expanded in terms of the Fourier series to generate values at any rotational speed and time interval. The generated mesh stiffness response was used on a dynamic model to generate the time and frequency response of the system. An additional data set was generated using feature-level data fusion. The two datasets were fed to the DCNN model to diagnose the crack faults and results were compared. It was shown that the feature-level data fusion method is very robust in diagnosing crack faults with good accuracy rates even with the presence of a high level of noise.
KW - data fusion
KW - deep convolutional neural network
KW - dynamic modeling
KW - finite element modeling
KW - planetary gear transmission
UR - http://www.scopus.com/inward/record.url?scp=85127400880&partnerID=8YFLogxK
U2 - 10.1115/1.4052364
DO - 10.1115/1.4052364
M3 - Article
AN - SCOPUS:85127400880
SN - 1048-9002
VL - 144
JO - Journal of Vibration and Acoustics, Transactions of the ASME
JF - Journal of Vibration and Acoustics, Transactions of the ASME
IS - 3
M1 - 031003
ER -