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首页> 外文会议>IEEE International Conference on Underwater System Technology: Theory and Applications >Novel data-driven performance degradation state monitoring of rolling bearing
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Novel data-driven performance degradation state monitoring of rolling bearing

机译：新型数据驱动性能下降状态监测滚动轴承

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摘要

A performance degradation state monitoring approach is introduced for rolling bearings (RB). Firstly, the time-domain features (TFs), frequency-domain features (FFs) and time-frequency-domain features (TFFs) of vibration signals (VS) are extracted to describe the performance degradation trend. Then, the performance degradation features are selected via the Spearman rank correlation coefficient (SRCC). Then, these selected features are fused to obtain health index (HI) using growing self-organizing map (GSOM), Finally, the identification of the degradation state is realized by the analysis of the monotonicity, recession and robustness of the HI and experimental results demonstrate the superiority of proposed method compared with other famous method.
机译：引入了用于滚动轴承（RB）的性能降级状态监测方法。首先，提取振动信号（VS）的时域特征（TFS），频域特征（FFS）和时间频域特征（TFF）以描述性能下降趋势。然后，通过Spearman等级相关系数（SRCC）选择性能劣化特征。然后，这些所选特征融合以获得使用生长自组织地图（GSOM）获得健康指数（HI），最后，通过分析HI和实验结果的单调性，衰退和鲁棒性来实现劣化状态的识别与其他着名方法相比，证明了所提出的方法的优越性。

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