Deep Convolutional Neural Network Framework for Diagnostics of Planetary Gearboxes Under Dynamic Loading With Feature-Level Data Fusion

Abstract

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.