Bearing Fault Diagnosis with Two-stage Multi-source Information Fusion Based on Improved Stacking Algorithm and D-S Evidence Theory
YUAN Ruiwei
CHEN Zhaoxiang
WEI Yujie
CHEN Zhen
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.
Keywords：two-stage information fusionfault diagnosisStacking algorithmDempster-Shafer evidence theoryimproved fusion rule
Publication Date：2025-08-30
Online Publishing Date：2025-09-22(First online date of this platform, not the publication date of the document)
Pages：12( 56-67 )