An Automatic FIR and DCGAN Model-based Fault Detection Framework for Key Components of Planetary Gearboxes under compartively Stable Conditions

An automatic fault detection framework for key components of planetary gearboxes is proposed in this work, in which the finite impulse response (FIR) band-pass filter is adopted to filter out the frequency components of the sun gear, planet gear, planet carrier, and ring gear, respectively, and then train the deep convolutional generative adversarial networks (DCGAN) model with the frequency components of each part separately. Afterwards, when completing the models training, the four FIR filter models and DCGAN models are combined to be a fault detection framework for key components of planetary gearboxes. The input of the framework is the vibration signals measured from a planetary gearbox, and the output are key component health conditions of the monitored planetary gearbox. The key advantage of this framework is to simultaneously and automatically identify faults, namely sun gear, planets, planet carriers and ring gear faults, with one detection.