基于递归分析和Stacking集成学习的轴承故障诊断方法  

Fault diagnosis for rolling bearings based on recurrence analysis and Stacking ensemble learning

作　　者：黄静静[1] 武文媗 田宇 王灿 王茂发 HUANG Jingjing;WU Wenxuan;TIAN Yu;WANG Can;WANG Maofa(School of Applied Science,Beijing Information Science&Technology University,Beijing 100192,China)

机构地区：[1]北京信息科技大学理学院,北京100192

出　　处：《南京信息工程大学学报》2025年第2期235-244,共10页Journal of Nanjing University of Information Science & Technology

基　　金：国家自然科学基金(12272058);北京市教育委员会科研计划项目(KM202411232006);基于华为云主机的《数学建模》课程建设(241100007123903);北京信息科技大学“青年骨干教师”支持计划(YBT202450)。

摘　　要：为了更加有效地挖掘滚动轴承信号中所具有的非线性信息并提高轴承故障诊断的准确率,提出一种基于递归分析和Stacking集成学习的轴承故障诊断方法.通过递归分析理论将轴承信号中的非线性信息映射到二维递归图中,分别从图像识别和递归定量分析的角度出发,对应建立了卷积神经网络和支持向量机两个子模型.使用Stacking方法将两个模型进行集成,可以在一定程度上结合两个模型的不同特点,充分发挥两个不同模型的优势.实验结果表明,该方法可以有效提高轴承振动信号的分类准确率,并在不同负载条件下表现出色且稳定,为轴承故障诊断提供了一种可靠的解决方案.Here,a bearing fault diagnosis method based on recurrence analysis and Stacking ensemble learning is proposed to effectively extract nonlinear information from rolling bearing signals and improve diagnostic accuracy.Firstly,the nonlinear information in bearing signals is mapped to a two-dimensional recurrence plot through the application of recurrence analysis theory.Convolutional Neural Network(CNN)and Support Vector Machine(SVM)models are established from the perspectives of image recognition and recurrence quantification analysis,respectively.Finally,the Stacking method is employed to integrate these two models,leveraging their respective strengths.Experimental results demonstrate that the proposed method significantly improves the classification accuracy of bearing vibration signals and exhibits excellent stability under varying load conditions,providing a reliable solution for bearing fault diagnosis.

关 键 词：故障诊断 滚动轴承 递归分析 Stacking集成学习 

分 类 号：TH133.3[机械工程—机械制造及自动化] TP183[自动化与计算机技术—控制理论与控制工程]