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.

[1]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[2]  P. D. McFadden.  A technique for calculating the time domain averages of the vibration of the individual planet gears and the sun gear in an epicyclic gearbox , 1991 .

[3]  Bolei Zhou,et al.  GAN Dissection: Visualizing and Understanding Generative Adversarial Networks , 2018, ICLR.

[4]  Ming J. Zuo,et al.  Vibration signal models for fault diagnosis of planetary gearboxes , 2012 .

[5]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[6]  Tom White,et al.  Generative Adversarial Networks: An Overview , 2017, IEEE Signal Processing Magazine.

[7]  Ming J. Zuo,et al.  Amplitudes of characteristic frequencies for fault diagnosis of planetary gearbox , 2018, Journal of Sound and Vibration.

[8]  Timo Aila,et al.  A Style-Based Generator Architecture for Generative Adversarial Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Robert B. Randall,et al.  Use of mesh phasing to locate faulty planet gears , 2019 .

[10]  Thomas W. Parks,et al.  Design of FIR filters in the complex domain , 1987, IEEE Trans. Acoust. Speech Signal Process..

[11]  Yaguo Lei,et al.  Condition monitoring and fault diagnosis of planetary gearboxes: A review , 2014 .

[12]  Ming J. Zuo,et al.  Dynamic modeling of gearbox faults: A review , 2018 .

[13]  Kesheng Wang,et al.  Motion Periods of Planet Gear Fault Meshing Behavior , 2018, Sensors.