Considering the nonlinear and nonstationary characteristics of rolling bearing vibration signals, we propose a rolling bearing fault diagnosis method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), refined composite multiscale fuzzy entropy (RCMFE), Laplace score (LS), and the particle swarm optimization-probabilistic neural network (PSO-PNN). First, the method employs CEEMDAN to decompose the vibration signal and select the intrinsic mode functions (IMFs) containing the primary fault information via the frequency-domain correlation coefficient method. Then, it uses RCMFE to extract the characteristic information from the selected IMF. In addition, it uses LS to select and construct low-dimensional sensitive feature vectors, which are incorporated into the PSO-PNN model for diagnostic analysis to realize the state recognition of rolling bearing. Finally, the effectiveness of the method is verified by the analysis of the experimental data.

中文翻译：

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基于CEEMDAN和精化复合多尺度模糊熵的滚动轴承故障诊断


针对滚动轴承振动信号的非线性和非平稳特性，提出一种基于自适应噪声完全系综经验模态分解（CEEMDAN）、精化复合多尺度模糊熵（RCMFE）、拉普拉斯评分（LS）和粒子群优化-概率神经网络（PSO-PNN）。首先，该方法采用CEEMDAN对振动信号进行分解，并通过频域相关系数法选择包含主要故障信息的本征模态函数（IMF）。然后，它使用 RCMFE 从选定的 IMF 中提取特征信息。此外，利用最小二乘法选择并构造低维敏感特征向量，将其纳入PSO-PNN模型进行诊断分析，实现滚动轴承的状态识别。最后通过实验数据分析验证了该方法的有效性。