108 / 2024-08-30 11:07:34
Remaining useful life prediction of rolling bearings based on global-local attention mechanism transformer with multi-source feature-weighted kernel principal component
remaining useful life, health index, spatiotemporal sequence feature weighting, global-local attention mechanism transformer, rolling bearings
全文录用
ZAIMIXIE / Guangdong Ocean University
CHUNMEIMO / Guangdong Ocean University
BAOZHUJIA / Guangdong Ocean University
Aiming at the health index (HI) is difficult to characterize when the rolling bearing state changes and the transformer network attention mechanism only focuses on the global feature information, resulting in the low prediction performance. This paper proposes a bearing RUL prediction method based on the global-local attention mechanism transformer with a multi-source feature-weighted kernel principal component. Squeeze and excitation networks (SENet) are applied to the adaptive assign weights to its multi-channel features. It combined with the distances of the multi-source features, which were calculated by the Mahalanobis distance (MD), to obtain the multi-source HI through the kernel principal component analysis algorithm (KPCA). It can balance multi-source features and retain important feature information. The global-local attention mechanism transformer method fuse and learn global and local spatiotemporal sequence features can improve bearing RUL prediction model performance. The proposed method was verified by experiments of the XJTU-SY dataset and the PHM2012 dataset and compared with the publicized methods. Results demonstrate the effectiveness and superiority of the proposed approach in terms of prediction accuracy.
重要日期
  • 会议日期

    10月31日

    2024

    11月03日

    2024

  • 09月30日 2024

    初稿截稿日期

  • 11月12日 2024

    注册截止日期

主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
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