噪声与振动控制2025，Vol.45Issue(3)：98-104,221,8.DOI:10.3969/j.issn.1006-1355.2025.03.016
基于GADF-Inception-SAM的转子故障诊断方法
Rotor Fault Diagnosis Based on GADF-Inception-SAM
摘要
Abstract
To address the problems that the existing rotor fault diagnosis methods lack the ability of multi-scale feature extraction and the strong noise leads to the low accuracy of fault classification,a fault diagnosis method based on GADF-In-ception-SAM was proposed.Firstly,the fault signal was transformed by GADF,and the images obtained by transforming dif-ferent sensor signals were horizontally spliced using a multi-sensor information fusion strategy.Then,the spliced images were input into the Inception-SAM model for classification and identification,in which the Inception module enhances the neural network ability for extracting multi-scale features,and the SAM enhances the generalization performance of the mod-el.The experimental results show that the proposed method can achieve 99.64%classification accuracy in rotor fault diagno-sis,and it has the highest fault classification accuracy compared with other image coding methods and neural network mod-els.Meanwhile,the anti-noise performance test proved that this method still has high accuracy in the high noise condition.关键词
故障诊断/格拉姆角差场/多传感器信息融合/锐度感知最小化/转子Key words
fault diagnosis/Gramian angular difference field/multi sensor information fusion/sharpness awareness minimization/rotor分类
信息技术与安全科学引用本文复制引用
伍济钢,张源,曾嘉..基于GADF-Inception-SAM的转子故障诊断方法[J].噪声与振动控制,2025,45(3):98-104,221,8.基金项目
国家自然科学基金资助项目(51775181) （51775181）
