北京交通大学学报2024，Vol.48Issue(5)：162-170,9.DOI:10.11860/j.issn.1673-0291.20230117
基于自适应分数阶循环平稳盲反卷积的滚动轴承故障诊断方法
Fault diagnosis method for rolling bearing based on adaptive fractial-order cyclostationary blind deconvolution
摘要
Abstract
In the industrial field,bearing fault signals are often subject to significant interference from strong background noise due to the harsh operating environment and complex working conditions of mechanical equipment,making it challenging to effectively extract fault characteristics.To address this,this paper proposes a fault diagnosis method for rolling bearing based on Adaptive γ-order Cyclo-stationary Blind Deconvolution(ACYCBDγ).First,a novel metric,the Local peak ratio(Lpr),is in-troduced to determine the optimal filter length.Then,the estimated fractional order based on a Gauss-ian smooth model is calculated to construct the fractional-order cyclostationary blind deconvolution.Fi-nally,the proposed model's performance is validated using both public and real-world datasets.The results demonstrate that ACYCBDγ achieves suppression ratios that are 20.61%,17.85%,and 44.95%higher than those of Minimum Entropy Deconvolution,Maximum Correlated Kurtosis Decon-volution,and Maximum Second-order Cyclostationarity Blind Deconvolution(CYCBD),respec-tively,on the public dataset.On the real dataset,the suppression ratios are improved by 53.63%,60.27%,and 55.16%,respectively.Under signal-to-noise ratios of-10 to-20 dB,ACYCBDγ en-hances the Lpr by 87.51%compared to CYCBD.Therefore,ACYCBDγ effectively reduces the im-pact of noise and interference,enabling the accurate extraction of bearing fault features in strong noise environments.关键词
轴承故障诊断/循环平稳/盲反卷积/特征提取Key words
bearing fault diagnosis/cyclostationarity/blind deconvolution/feature extraction分类
机械制造引用本文复制引用
吴怡,王金海,杨建伟,徐丹萍..基于自适应分数阶循环平稳盲反卷积的滚动轴承故障诊断方法[J].北京交通大学学报,2024,48(5):162-170,9.基金项目
国家自然科学基金(52205083,52272385,51975038) （52205083,52272385,51975038）
北京市自然科学基金(L211008) National Natural Science Foundation of China(52205083,52272385,51975038) （L211008）
Beijing Natural Science Foundation(L211008) （L211008）
