As a useful data augmentation technique, generative adversarial networks have been successfully applied in fault diagnosis field. But traditional generative adversarial networks can only generate one category fault signals in one time, which is time-consuming and costly. To overcome this weakness, we develop a novel fault diagnosis method which combines conditional generative adversarial networks and stacked autoencoders, and both of them are built by stacking one-dimensional full connection layers. First, conditional generative adversarial networks is used to generate artificial samples based on the frequency samples, and category labels are adopted as the conditional information to simultaneously generate different category signals. Meanwhile, spectrum normalization is added to the discriminator of conditional generative adversarial networks to enhance the model training. Then, the augmented training samples are transferred to stacked autoencoders for feature extraction and fault classification. Finally, two datasets of bearing and gearbox are employed to investigate the effectiveness of the proposed conditional generative adversarial network–stacked autoencoder method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Data augment method for machine fault diagnosis using conditional generative adversarial networks


    Contributors:
    Wang, Jinrui (author) / Han, Baokun (author) / Bao, Huaiqian (author) / Wang, Mingyan (author) / Chu, Zhenyun (author) / Shen, Yuwei (author)


    Publication date :

    2020-10-01


    Size :

    9 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    On Generating Parametrised Structural Data Using Conditional Generative Adversarial Networks

    Tsialiamanis, G. / Wagg, D. J. / Dervilis, N. et al. | British Library Conference Proceedings | 2021


    REALISTIC ULTRASONIC ENVIRONMENT SIMULATION USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS

    Pöpperli, Maximilian / Gulagundi, Raghavendra / Yogamani, Senthil et al. | British Library Conference Proceedings | 2019


    Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks

    Popperli, Maximilian / Gulagundi, Raghavendra / Yogamani, Senthil et al. | IEEE | 2019



    Recurrent Conditional Generative Adversarial Networks for Autonomous Driving Sensor Modelling

    Arnelid, Henrik / Zec, Edvin Listo / Mohammadiha, Nasser | IEEE | 2019