工业工程2025，Vol.28Issue(4)：56-67,12.DOI:10.3969/j.issn.1007-7375.240326
基于改进Stacking与D-S证据理论的两阶段多源信息融合轴承故障诊断
Bearing Fault Diagnosis with Two-stage Multi-source Information Fusion Based on Improved Stacking Algorithm and D-S Evidence Theory
摘要
Abstract
To address the limitations of fault diagnosis performance based on a single sensor and a single fault feature,a two-stage multi-source sensor information fusion method for bearing fault diagnosis is proposed.First,considering sample imbalance characteristics and multi-feature correlation,an improved Stacking algorithm is proposed to construct a one-dimensional residual feature fusion network.This network improves the training of multi-sensor features and achieves fault feature fusion.Then,to cope with the uncertainty of multi-source sensor information,an improved evidence fusion rule considering the amount of evidence and its reliability is proposed to realize fault decision fusion based on Dempster-Shafer evidence theory.A case study on rolling bearings shows that the proposed method achieve all above 0.98 on average accuracy,precision,recall and f1-score under different working conditions.Compared with other seven methods,the proposed method shows superior performance and good generalization.关键词
两阶段信息融合/故障诊断/Stacking算法/Dempster-Shafer证据理论/改进合成规则Key words
two-stage information fusion/fault diagnosis/Stacking algorithm/Dempster-Shafer evidence theory/improved fusion rule分类
管理科学引用本文复制引用
袁锐炜,陈兆祥,卫宇杰,陈震..基于改进Stacking与D-S证据理论的两阶段多源信息融合轴承故障诊断[J].工业工程,2025,28(4):56-67,12.基金项目
上海市自然科学基金资助项目(23ZR1428100) （23ZR1428100）
