南京信息工程大学学报2025，Vol.17Issue(2)：235-244,10.DOI:10.13878/j.cnki.jnuist.20240617001
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
摘要
Abstract
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 ap-plication 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 relia-ble 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Key words
fault diagnosis/rolling bearing/recurrence analysis/Stacking ensemble learning分类
机械工程引用本文复制引用
黄静静,武文媗,田宇,王灿,王茂发..基于递归分析和Stacking集成学习的轴承故障诊断方法[J].南京信息工程大学学报,2025,17(2):235-244,10.基金项目
国家自然科学基金(12272058) （12272058）
北京市教育委员会科研计划项目(KM202411232006) （KM202411232006）
基于华为云主机的《数学建模》课程建设(241100007123903) （241100007123903）
北京信息科技大学"青年骨干教师"支持计划(YBT202450) （YBT202450）
