Fault diagnosis method for rolling bearing based on adaptive fractial-order cyclostationary blind deconvolution
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.
万方数据
维普
