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Rolling bearing fault diagnosis based on optimized A-BiLSTM
更新时间：2025-05-26

    • Rolling bearing fault diagnosis based on optimized A-BiLSTM

    • “The latest research has found that an attention bidirectional long short-term memory network optimized based on the Honey Badger algorithm can achieve a 99.5% accuracy in diagnosing rolling bearing faults and has strong generalization ability.”
    • Journal of Jilin University(Engineering and Technology Edition)   Vol. 54, Issue 8, Pages: 2156-2166(2024)
    • 作者机构：

      1.兰州理工大学 电气工程与信息工程学院，兰州 730050

      2.甘肃省工业过程控制重点实验室，兰州 730050

      3.兰州理工大学 电气与控制工程国家级实验教学示范中心，兰州 730050

    • DOI：10.13229/j.cnki.jdxbgxb.20221339    

      CLC： TH133.3;TP183

    • Received：18 October 2022，

      Published：01 August 2024

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  • YU Ping,ZHAO Kang,CAO Jie.Rolling bearing fault diagnosis based on optimized A-BiLSTM[J].Journal of Jilin University(Engineering and Technology Edition),2024,54(08):2156-2166. DOI： 10.13229/j.cnki.jdxbgxb.20221339.

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