一种基于多通道马尔可夫变迁场的故障诊断方法

A fault diagnosis method based on multi Markov transition field

深度学习在故障诊断中有良好的诊断能力与泛化能力,但大部分工作是直接从卷积层面上提取信号特征图,使邻近信号点未被考虑,并且采样频率不同也会对特征提取有影响。为此,本文基于MTF以及ResNet18算法提出了M2TF-ResNet算法。本文在凯斯西储大学(CWRU)轴承数据集中进行了大量实验。通过验证得出:该算法可适应不同采样频率下信号的特征提取,避免训练过拟合,并且与其他故障诊断方式相比,该算法在诊断率上的优势更突出。

Deep learning has good diagnostic capabilities and generalization capabilities in fault diagnosis,but most of the work is to directly extract signal feature maps from the convolutional layer so that adjacent signal points are not considered,and different sampling frequencies will also affect feature extraction.Therefore,the M2TF-ResNet algorithm was proposed based on the MTF and ResNet18 algorithm.Many experiments were carried out in Case Western Reserve University(CWRU)bearing dataset.Through the verification,it can adapt to the signal feature extraction under different sampling frequencies and avoid over-fitting training.And compared with other fault diagnosis methods,it has more prominent advantages in the diagnosis rate.