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Semi-supervised multitask deep convolutional generative adversarial network for unbalanced fault diagnosis of rolling bearing

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Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

In the process of data collection of rolling bearing, it is inevitable to get unbalanced data, including unlabeled samples, relatively few fault samples and abundant normal samples. To further improve sample efficiency, this paper proposes semi-supervised multitask deep convolutional generative adversarial network (SM-DCGAN). One-dimensional raw vibration signals are transformed into two-dimensional grayscale images. Different from traditional DCGAN, the proposed SM-DCGAN fuses the tasks of discrimination and classification to form multitask discriminator, which can simultaneously train labeled and unlabeled samples. Subsequently, knowledge distillation method is used to achieve simple fault classifier from multitask discriminator. Then, unbalanced samples can be expanded by generator in SM-DCGAN. Unlabeled samples, labeled samples and expanded samples are input into the simple fault classifier to realize semi-supervised fault diagnosis. The results of rolling bearing fault diagnosis experiments fully prove that the proposed SM-DCGAN has high accuracy, great robustness and sufficient stability for unbalanced fault samples.

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Funding

This work is financially supported in part by Joint Funds of the National Natural Science Foundation of China (NSFC) under Grant U1833110.

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Authors and Affiliations

  1. College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, China

    Changchang Che

  2. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Huawei Wang, Ruiguan Lin & Xiaomei Ni

Authors
  1. Changchang Che
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  2. Huawei Wang
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  3. Ruiguan Lin
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  4. Xiaomei Ni
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Contributions

CC summarized the existing researches and contributed ideas that apply the intelligent learning to the fault diagnosis and supplied experimental data analysis. HW contributed ideas about data applying and designed the framework of algorithm. RL and XN provided the simulation experimental supports and contributed to the data acquisition and processing.

Corresponding author

Correspondence to Changchang Che.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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All authors participated on the manuscript draft and approved the submission.

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All the authors agreed to publish the manuscript.

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The paper doesn’t involve human subjects. There is no risk of damage to the University’s reputation because of the sensitivity of the chosen topic.

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Technical Editor: Samuel da Silva.

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Che, C., Wang, H., Lin, R. et al. Semi-supervised multitask deep convolutional generative adversarial network for unbalanced fault diagnosis of rolling bearing. J Braz. Soc. Mech. Sci. Eng. 44, 276 (2022). https://doi.org/10.1007/s40430-022-03576-x

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  • DOI: https://doi.org/10.1007/s40430-022-03576-x

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