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Transfer fault prognostic for rolling bearings across different working conditions: a domain adversarial perspective

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Abstract

Modern machines generally operate under varying working conditions, which induces significant data distribution discrepancies in the gathered condition monitoring signals. However, most of the existing machine learning–based methods, especially deep learning (DL)–based fault prognostic methods, neglect the data distribution discrepancy between the training and testing data. As a result, most of the existing DL-based methods can only generalize well under identical working conditions, which is infeasible in real engineering practice. To solve this critical issue, a novel dual-branch neural network with a domain adversarial module is developed to achieve transfer fault prognostics across different operating conditions. A dual-branch-based DL model is first utilized to extract abundant degradation features from the heterogeneous inputs. Then, the domain adversarial technique is employed to solve the significant distribution discrepancy problem existing across different operating conditions. The proposed approach is validated experimentally through two rolling element bearing open-sourced datasets, i.e., the XJTU-SY bearing dataset and the PRONOSTIA bearing dataset. The experimental results demonstrate that the proposed method can accurately achieve the transfer fault prognostic task without any labelled data in the target domain, and performance comparisons with other state-of-the-art approaches are also presented.

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Data availability

XJTU-SY bearing datasets are available at https://biaowang.tech/xjtu-sy-bearing-datasets/, which are provided by the Institute of Design Science and Basic Component at Xi’an Jiaotong University (XJTU), Shaanxi, P. R. China, and the Changxing Sumyoung Technology Co., Ltd. (SY), Zhejiang, P. R. China. PRONOSTIA bearing datasets are available at https://github.com/wkzs111/phm-ieee-2012-data-challenge-dataset, which are provided by the FEMTO-ST Institute.

Code availability

Code can be available upon request.

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Acknowledgements

The authors would like to give many thanks to Xi’an Jiaotong University, Changxing Sumyoung Technology Co., Ltd, and FEMTO-ST institute for sharing experimental setup and datasets.

Funding

This work was sponsored by the National Natural Science Foundation of China (no. 62003377), China Postdoctoral Science Foundation (no. 2021M693607), Ministry of Natural Resources in Guangdong Province (no. GDNRC[2021]38), and Guangdong Provincial Department of Science and Technology (no. 2019B090904005).

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Authors and Affiliations

  1. School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen, 518107, China

    Cheng-Geng Huang, Changhao Men, Mohammad Yazdi, Yu Han & Weiwen Peng

  2. Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology, Guangzhou, 510006, China

    Cheng-Geng Huang, Changhao Men, Yu Han & Weiwen Peng

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  1. Cheng-Geng Huang
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  2. Changhao Men
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  3. Mohammad Yazdi
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  4. Yu Han
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  5. Weiwen Peng
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Contributions

Cheng-Geng Huang: conceptualization; methodology; writing–original draft. Changhao Men: experimental validation. Mohammad Yazdi: writing–review and editing. Yu Han: resources, supervision, and funding. Weiwen Peng: project administration and funding.

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Correspondence to Yu Han.

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Huang, CG., Men, C., Yazdi, M. et al. Transfer fault prognostic for rolling bearings across different working conditions: a domain adversarial perspective. Int J Adv Manuf Technol (2022). https://doi.org/10.1007/s00170-022-09452-1

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