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Author:

Yu, Jun (Yu, Jun.) | Xu, Yonggang (Xu, Yonggang.) | Liu, Ke (Liu, Ke.) (Scholars:刘克)

Indexed by:

EI Scopus SCIE

Abstract:

Owing to high transmission ratio and compact structure, planetary gearboxes are widely used in industrial applications. However, due to running under noisy environment and time-varying rotational speed conditions, the acquired vibration signals are nonstationary, which seriously degrade the performance of fault diagnosis methods of planetary gears. To address this problem, a fault diagnosis method of planetary gears using a stacked denoising autoencoder (SDAE) and a gated recurrent unit neural network (GRUNN) is proposed in this paper. First, a hybrid model based on SDAE and GRUNN is developed to remove noise components from input data and process pre- and post-correlation time-series data. The training samples for planetary gear fault diagnosis are regarded as the input data of the developed hybrid model to automatically extract robust fault features. Then, the training process of the developed hybrid model is presented. The Adam optimization algorithm is utilized to optimize the parameters, and the dropout technique is employed to prevent from overfitting. Finally, a softmax classifier is employed to identify planetary gear states in test samples. The effectiveness of the proposed method is validated through a fault diagnosis experiment of planetary gears. The experimental results show that the proposed method possesses strong anti-noise ability and adaptability to time-varying rotational speed.

Keyword:

planetary gear fault diagnosis time-varying rotational speed noisy environment stacked denoising autoencoder gated recurrent unit neural network

Author Community:

  • [ 1 ] [Yu, Jun]Harbin Univ Sci & Technol, Sch Automat, Harbin 150080, Heilongjiang, Peoples R China
  • [ 2 ] [Liu, Ke]Harbin Univ Sci & Technol, Sch Automat, Harbin 150080, Heilongjiang, Peoples R China
  • [ 3 ] [Xu, Yonggang]Beijing Univ Technol, Key Lab Adv Mfg Technol, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Yu, Jun]Harbin Univ Sci & Technol, Sch Automat, Harbin 150080, Heilongjiang, Peoples R China

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Source :

MEASUREMENT SCIENCE AND TECHNOLOGY

ISSN: 0957-0233

Year: 2019

Issue: 9

Volume: 30

2 . 4 0 0

JCR@2022

ESI Discipline: ENGINEERING;

ESI HC Threshold:136

JCR Journal Grade:2

Cited Count:

WoS CC Cited Count: 40

SCOPUS Cited Count: 42

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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