基于随机共振和随机森林的轴承故障诊断

Bearing Fault Diagnosis Based on Stochastic Resonance and Random Forest

本文研究了基于随机共振和随机森林的轴承故障诊断方法。首先将原始的轴承故障数据分为训练集和测试集,然后将数据集通过随机共振处理以增强弱故障特征,之后将增强后的故障数据输入随机森林中进行模型训练,通过调整合适的模型参数,计算出最终正确率。结果表明,与传统的机器学习相比,本文提出的方法具有更高的识别准确率,可为智能印刷机的设计及故障诊断提供理论指导和技术支持。

In this study,the bearing fault diagnosis method based on stochastic resonance and random forest was mainly researched.Firstly,the original bearing fault data was divided into training set and test set,then the data set was subjected to stochastic resonance processing to enhance the weak fault feature.The enhanced fault data was input into the random forest for model training,and the appropriate model parameters were adjusted to calculate the final correct rate.This method proposed has higher recognition accuracy than traditional machine learning.This research provides theoretical guidance and technical support for the design and fault diagnosis of printing machine.