Intelligent fault diagnosis of planetary gearboxes under time-varying condition based on bilateral adversarial encoder

ZHAO Chuan,FENG Zhipeng,ZHANG Yinglin,WANG Kun

Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (20) : 158-167.

PDF(3993 KB)
PDF(3993 KB)
Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (20) : 158-167.

Intelligent fault diagnosis of planetary gearboxes under time-varying condition based on bilateral adversarial encoder

  • ZHAO Chuan1,2,  FENG Zhipeng2,ZHANG Yinglin1,3,WANG Kun1,4
Author information +
History +

Abstract

Characteristic frequencies of planetary gearboxes are time-varying when they are running under time-varying condition, and conventional statistics are unsuitable to explore time variability and potential properties of nonstationary signals.These make fault diagnosis of a planetary gearbox manually difficult.In order to address these issues, a bilateral adversarial encoder model was proposed.Firstly, a time-frequency representation image of a sample was obtained to reveal the time-varying frequencies.Then, an encoder and a decoder were established.The input of the encoder and the output of the decoder were utilized to train discriminator 1 to ensure that the reconstructed signal follows the distribution of original signal, and the features of an image are effectively extracted.Besides, a Gaussian mixture distribution was established and samples were collected from the Gaussian mixture distribution according to the label information.Discriminator 2 is utilized to make the extracted features follow the distribution of the samples in order to enhance the discriminability of features among different classes by controlling the mixture distribution.Finally, a Softmax classifier was trained by the enhanced features and the testing features were identified.This method was validated via planetary gearbox data set.The results indicate that the reconstructed signal can be made by an adversarial game to follow the distribution of the original signal and the features can be controlled by a Gaussian mixture distribution to improve their clustering performance and diagnose the gear faults accurately.In comparison with other methods, it works better to some degree.

Key words

planetary gearbox / intelligent fault diagnosis / Gaussian mixture distribution / bilateral adversarial encoder / time-varying condition

Cite this article

Download Citations
ZHAO Chuan,FENG Zhipeng,ZHANG Yinglin,WANG Kun. Intelligent fault diagnosis of planetary gearboxes under time-varying condition based on bilateral adversarial encoder[J]. Journal of Vibration and Shock, 2021, 40(20): 158-167

References

[1]COOLEY C G, PARKER R G.A review of planetary and epicyclic gear dynamics and vibrations research [J].Applied Mechanics Reviews, 2014, 66(4): 1-15.
[2]LIANG X H, ZUO M J, FENG Z P.Dynamic modelling of gearbox faults: A review [J].Mechanical Systems and Signal Processing, 2018, 98: 852-876.
[3]FENG Z P, CHEN X W, LIANG M.Time-frequency demodulation analysis based on iterative generalized demodulation for fault diagnosis of planetary gearbox under nonstationary conditions [J].Mechanical Systems and Signal Processing, 2015, 62/63: 54-74.
[4]FENG Z P, CHEN X W, LIANG M.Joint envelope and frequency order spectrum analysis based on iterative generalized demodulation for planetary gearbox fault diagnosis under nonstationary conditions [J].Mechanical Systems and Signal Processing, 2016, 76/77: 242-264.
[5]CHEN X W, FENG Z P.Iterative generalized time-frequency reassignment for planetary gearbox fault diagnosis under nonstationary conditions [J].Mechanical Systems and Signal Processing, 2016, 80: 429-444.
[6]CHEN X W, FENG Z P.Time-frequency analysis of torsional vibration signals in resonance region for planetary gearbox fault diagnosis under variable speed conditions [J].IEEE Access, 2017, 5: 21918-21926.
[7]FENG K, WANG K S, NI Q.A phase angle based diagnostic scheme to planetary gear faults diagnostics under non-stationary operational conditions [J].Journal of Sound and Vibration, 2017, 408: 190-209.
[8]GUAN Y P, LIANG M, NECSULESCU D S.A velocity synchro-squeezing transform for fault diagnosis of planetary gearboxes under nonstationary conditions [J].Journal of Mechanical Engineering Science, 2017, 231(15): 2868-2884.
[9]HU Y, TU X T, LI F C.Joint high-order synchro-squeezing transform and multi-taper empirical wavelet transform for fault diagnosis of wind turbine planetary gearbox under non-stationary conditions [J].Sensors, 2018, 18: 1-18.
[10]LI Y B, LI G Y, YANG Y T, et al.A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy [J].Mechanical Systems and Signal Processing, 2017, 105: 319-337.
[11]LIU L B, LIANG X H, ZUO M J.A dependence-based feature vector and its application on planetary gearbox fault classification [J].Journal of Sound and Vibration, 2018, 431: 192-211.
[12]ZHANG K, TANG B P, QIN Y, et al.Fault diagnosis of planetary gearbox using a novel semi-supervised method of multiple association layers networks [J].Mechanical Systems and Signal Processing, 2019, 131: 243-260.
[13]LI Q K, TANG B P, LEI D, et al.Deep balanced domain adaption neural networks for fault diagnosis of planetary gearboxes with limited labelled data [J].Measurement, 2020, 156:1-10.
[14]MAKHZANI A, SHLENS J, JAITLY N.Adversarial auto-encoders, arXiv:1511.05644v2 [cs.LG], 2016.
[15]HONG Y, HWANG U, YOO J.How generative adversarial networks and their variants work: An Overview [J].ACM Computing Surveys, 2019, 52(1): 1-10.
[16]WANG Z R, WANG J, WANG Y R.An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition [J].Neurocomputing, 2018, 310: 213-222.
[17]MAO W, LIU Y M, DING L.Imbalanced fault diagnosis of rolling bearing based on generative adversarial network: a comparative study [J].IEEE Access, 2019, 7: 9515-9530.
[18]WEN L, GAO L, LI X.A new deep transfer learning based on sparse auto-encoder for fault diagnosis [J].IEEE Transactions on Systems Man Cybernetics-systems, 2019, 7(1):136-144.
[19]冯志鹏, 赵镭镭, 褚福磊.行星齿轮箱齿轮局部故障振动频率特征[J].中国电机工程学报, 2013, 33(5):123-126.
FENG Zhipeng,ZHAO Leilei,CHU Fulei.Vibration spectral characteristics of localized gear fault of planetary gearboxes[J].Proc CSEE, 2013, 33(5):123-126.
[20]ZHAO C, FENG Z P.Application of multi-domain sparse features for fault identification of planetary gearbox [J].Measurement, 2017, 104: 169-179.
PDF(3993 KB)

Accesses

Citation

Detail

Sections
Recommended

/