基于变分模态分解和卷积神经网络融合的滚动轴承故障诊断方法

A VMD and CNN Combined Fault Diagnosis Method for Rolling Bearings

针对在强烈背景噪声影响下的滚动轴承故障特征提取困难,提出了一种基于变分模态分解与卷积神经网络融合的滚动轴承故障诊断方法。将原始振动信号分解为多个模态分量,结合皮尔逊相关系数作为自动分解终止阈值和最优模态分量选取指标;针对轴承故障特征构建卷积神经网络,将最优模态分量作为输入以提取、分类故障类型。试验结果表明,所提方法能够精确诊断滚动轴承故障,为强噪声影响下的滚动轴承故障识别提供了新的思路。

Aiming at the difficulty of extracting fault features of rolling bearings under the influence of strong background noise,a rolling bearing fault diagnosis method based on the fusion of variational mode decom⁃position(VMD)and convolutional neural network(CNN)is proposed.After decomposing the original variation signal into multiple components,the proposed method employs the Pearson correlation coefficient as the auto⁃matic decomposition termination threshold and the optimal modal component selection index;a convolutional neural network is constructed according to bearing fault features and the optimal modal component is used as the input to extract and classify the fault types.The experiments validate that the proposed method can accurately di⁃agnose the rolling bearing faults,which is validated as a new method for rolling bearing fault diagnosis regarding strong background noise.