结合 DSHDD 和模糊评价的滚动轴承退化状态在线识别

Online identification of rolling bearing degradation state based on DSHDD and fuzzy evaluation

  • 摘要: 提出一种用自适应噪声辅助的集合经验模态分解(CEEMDAN)和能量熵结合提取振动信号的特征的方法,将特征输入到双超球数据域描述(DSHDD)模型中,再将得到的结果输入到隶属度函数中,计算隶属度,以此作为性能退化评估的指标。使用3c设置自适应阈值,确定轴承早期失效阈值。用CEEMDAN和Hilbert包络解调的方法验证评估结果的正确性。最后利用美国辛辛那提大学的轴承全寿命周期数据验证该模型的有效性和实用性。

     

    Abstract: In the long term use process,the performance of roling bearing willbe degraded to different degrees.If the degradationstate of roling bearing can be identified online,accidents can be effectively prevented.In this paper,an adaptive noiseassisted collective empirical mode decomposition(CEEMDAN)method combined with energy entropy is proposed to extract the characteristics of vibration signals,and then the characteristics are input into the DSHDD model,and the obtained results are input into themembership function to calculate the membership,which can be used as the evaluation index of performance degradation.An adaptive threshold is set using 3o to determine the bearing's early failure threshold.CEEMDAN and Hibert envelope demodulationmethods are used to verify the correctness of the evaluation results.The validity and practicability of the model are verifed by usingthe bearing life cycle data from the University of Cincinnati.

     

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