【摘要】 针对石化机组轴承振动信号难以自动区分的问题，提出一种基于改进的自适应噪声完备集合经验模态分解(CEEMDAN)与关联维数的石化轴承故障特征提取方法。选取某故障诊断重点实验室实测的轴承故障数据中4种工况下的轴承振动信号进行测试分析，采用改进的CEEMDAN分解测得的振动信号得到多个模态分量IMF,对得到的高频分量进行叠加求和后求取数据的嵌入维数和延迟时间并进行相空间重构，结合G-P算法求不同嵌入维数下的关联维数进行特征提取。通过极限学习机进行实验，准确率达到92.5%,证明了方法的有效性。更多还原

【Abstract】 An improved adaptive noise-complete ensemble empirical modal decomposition(CEEMDAN) with correlation dimension for petrochemical bearing fault feature extraction was proposed for the problem that the bearing vibration signals of petrochemical units are difficult to be distinguished automatically.The bearing vibration signals under four working conditions in the bearing fault data measured by a key laboratory of fault diagnosis were selected and tested for analysis.The measured vibration signals were decomposed by using the modified CEEMDAN to obtain multiple modal components IMF.The selected components were superimposed and summed to obtain the embedding dimension and delay time of the data and phase space reconstruction was performed.Combined with G-P algorithm, the correlation dimension under different embedding dimensions was found for feature extraction.And the experimental accuracy is 92.5% by the extreme learning machine, which proves the effectiveness of the method.更多还原