基于ECA-ResNet与CEEMDAN能量熵的轴承故障诊断

为解决轴承故障诊断中故障分类模型参数多且泛化性能弱、故障识别率低、识别速度慢的问题，设计一种基于深度学习模型ECA-ResNet、完全噪声辅助聚合经验模态分解与麻雀搜索算法优化的支持向量机(SSA-SVM)的故障诊断方法。通过ECA-ResNet对轴承信号进行建模以提取频域故障特征；将频域特征与CEEMDAN提取的能量熵以及传统信号的时域特征共同构成特征矩阵；通过SSA-SVM进行故障类型识别。结果表明：与传统故障特征提取方式相比，所提出的轴承故障诊断方法能得到良好的诊断效果，轴承故障识别率和分类速度较高。

Bearing Fault Diagnosis Based on ECA-ResNet and CEEMDAN Energy Entropy

In order to solve the problem of bearing fault diagnosis with many fault classification model parameters and weak generalization performance,low fault recognition rate and slow speed,a fault diagnosis method based on deep learning model ECA-ResNet,complete ensemble empirical mode decomposition with adaptive noise and the support vector machines optimized by sparrow search algorithm(SSA-SVM) was designed.ECA-ResNet was used to model the bearing signal to extract the frequency domain fault characteristics;the frequency domain characteristics,the energy entropy extracted by using CEEMDAN and the time domain characteristics of the traditional signal were combined to form a feature matrix;the fault types were identified by using SSA-SVM.The results show that compared with the traditional fault feature extraction method,by using the proposed bearing fault diagnosis method,a good diagnosis effect can be get,and the bearing fault recognition rate and classification speed are high.