基于CEEMDAN和CNN-LSTM的滚动轴承故障诊断

针对滚动轴承工作环境复杂，轴承振动信号受噪声干扰难以提取故障特征以及传统故障诊断算法准确率较低的问题，提出了利用自适应噪声完备集合经验模态分解算法（CEEMDAN）联合卷积神经网络（CNN）内嵌长短期记忆神经网络（LSTM）的滚动轴承故障诊断方法。首先，利用CEEMDAN算法对轴承原始振动信号进行分解得到本征模态函数（IMF）；然后计算重构后的信号的排列熵，归一化后作为特征向量；最后将特征向量输入至CNN-LSTM结合建立的深度学习模型中进行诊断识别。结果表明：所提方法具有更快的拟合速度和更高的准确率，平均故障诊断准确率达到98.63%。

Fault diagnosis of rolling bearing based on CEEMDAN and CNN-LSTM

In view of the complex working environment of rolling bearings, the difficulty of extracting fault features from bearing vibration signals due to noise interference, and the low accuracy of traditional fault diagnosis algorithms, a rolling bearing fault diagnosis method based on complete ensemble empirical mode decomposition with adaptive noise analysis algorithm (CEEMDAN) combined with convolution neural network (CNN) and embedded long short-term memory neural network (LSTM) is proposed. Firstly, the original vibration signal of the bearing is decomposed by CEEMDAN algorithm to obtain the intrinsic mode function (IMF); Then the permutation entropy of the reconstructed signal is calculated and normalized as the eigenvector; Finally, the eigenvector is input into the deep learning model established by CNN-LSTM for diagnosis and recognition. The results show that the proposed method has faster fitting speed and higher accuracy, and the average fault diagnosis accuracy rate reaches 98.63%.