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提出一种基于集合平均经验分解的盲源分离算法(EEMD-BSS)的滚动轴承故障诊断方法。这种集合平均经验分解算法相比较于经验模态分解算法(EEMD)更具有优越性,它能更好地克服信号之间的混叠效应。以振动筛轴承的故障为案例,先估计出振动筛轴承故障时振动的源数,再利用基于集合平均经验模态分解(EEMD)的盲源分离(BSS)算法,可以有效地分析出振动筛轴承的故障特征,因而具有重要的实用价值。
A rolling bearing fault diagnosis method based on a set of EEMD-BSS is proposed. Compared with the empirical mode decomposition algorithm (EEMD), this set of average empirical decomposition algorithm is more superior, it can better overcome the aliasing effect between signals. Taking the failure of the vibrating screen bearing as an example, the number of vibration sources at the bearing failure of the vibrating screen is first estimated. Then the blind source separation (BSS) algorithm based on collective average empirical mode decomposition (EEMD) is used to analyze the vibration screen Bearing fault features, which has important practical value.