Adaptive diagnosis method based on gearbox unbalanced fault data
TIAN Juan
XIE Gang
ZHANG Shun
WANG Yufei
Abstract：[Objective]The existing intelligent fault diagnosis methods face challenges,such as model training relying on a large amount of labeled data,difficulty in obtaining fault data with different occurrence probabilities,and insufficient consider-ation of the impact of operating conditions.To address these challenges,a novel gearbox diagnosis method for adaptive inter-class and intra-class unbalanced fault data under varying working conditions was proposed.[Methods]Firstly,a gated local con-nection network was utilized to reduce the reliance on the labeled data and extract intrinsic features directly from the original da-ta.Secondly,a parallel mechanism of external and internal attention was designed to consider the distribution differences among inter-class and intra-class faults under different working conditions,adjusting the weights of extracted features accordingly.Fi-nally,focal loss function was employed to focus on minority and challenging samples,enabling high-quality mining of unbal-anced diagnostic information.[Results]The proposed method is demonstrated by six unbalanced gearbox datasets,which shows great effectiveness and superiority in identifying unbalanced fault data.
Keywords：Fault diagnosisInter-class and intra-class imbalancesGated local connection networkAttention parallel mechanismFocal loss
Publication Date：2025-01-14
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：10( 153-162 )