Data augmentation and intelligent fault diagnosis of planetary gearbox using ILoFGAN under extremely limited samples

Chen, Mingzhi and Shao, Haidong and Dou, Haoxuan and Li, Wei and Liu, Bin (2023) Data augmentation and intelligent fault diagnosis of planetary gearbox using ILoFGAN under extremely limited samples. IEEE Transactions on Reliability, 72 (3). pp. 1029-1037. ISSN 0018-9529 (https://doi.org/10.1109/TR.2022.3215243)

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Abstract

Although the existing generative adversarial networks (GAN) have the potential for data augmentation and intelligent fault diagnosis of planetary gearbox, it remains difficult to deal with extremely limited training samples and effectively fuse the representative and diverse information. To tackle the abovementioned challenges, an improved local fusion generative adversarial network is proposed. Time-domain waveforms are first transformed into the time–frequency diagrams to highlight the fault characteristics. Subsequently, a local fusion module is used to fully utilize extremely limited samples and fuse the local features. Finally, a new generator embedded with multi-head attention modules is constructed to effectively improve the accuracy and flexibility of the feature fusion process. The proposed method is applied to the analysis of planetary gearbox vibration signals. The results show that the proposed method can generate a large number of samples with higher similarity and better diversity compared with the existing mainstream GANs using six training samples in each type. The generated samples are used to augment the limited dataset, prominently improving the accuracy of the fault diagnosis task.

ORCID iDs

Chen, Mingzhi, Shao, Haidong, Dou, Haoxuan, Li, Wei and Liu, Bin ORCID logoORCID: https://orcid.org/0000-0002-3946-8124;
  • Item type:Article
    ID code:82846
    Dates:
    Date
    Event
    September 2023
    Published
    27 October 2022
    Published Online
    12 October 2022
    Accepted
    Subjects:Technology
    Department:Strathclyde Business School > Management Science
    Depositing user: Pure Administrator
    Date deposited:20 Oct 2022 10:17
    Last modified:20 Nov 2024 04:09
    URI:https://strathprints.strath.ac.uk/id/eprint/82846
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