Fault Diagnosis Method Of Rotating Machinery Based On Non-Convex Constraints

As the important components of mechanical equipment,gears and rolling bearings are easy to suffer from damage under complex running conditions.And it is a difficult problem to extract the fault feature component in the early failure stage or under strong background noise.For the sparseness and structural characteristics of rotating machinery fault signals,the fault diagnosis method based on fault feature enhancement and non-convex constraint is deeply researched in this paper.For the periodic impulse signals induced by gear local damage,a fault feature extraction method based on sliding window correlation and Overlapping Group Shrinkage(OGS)is proposed.The fault impulsive pattern learnt by shift-invariant K-SVD algorithm is used for sliding window correlation with original signal in this method firstly,thereby the impulsive feature that buried in heavy noise is enhanced in correlation signal.The fault periodic feature are extracted directly by non-convex regularized OGS algorithm from the correlation signal.The influence of those parameters in the proposed method is analyzed too.The effectiveness of the proposed method is verified by gear localized fault simulation and experimental data,and the analysis on the normal gear signal also proves that the method does not cause misdiagnosis.For the fault diagnosis of rolling bearings,the effect of sparse representation largely depends on the signal-to-noise ratio(SNR)and constructed dictionary.To address these challenges,a fault diagnosis method based on feature enhancement and non-convex regularization is proposed this paper.Utilizing the structure characteristic of impulse response signal,that is,peaks and troughs appear alternately with the same intervals,a structure characteristic matrix is constructed for enhancing the weak impulse feature.In addition,a Fused Moreau-enhanced Total Variation Denoising(FMTVD)penalty is developed to avoid the dictionary construction problem and induce the sparsity.The new cost function considers the sparsity of both the fault signal and its differential form,and its solution is derived according to the alternating direction method of multipliers(ADMM).It is found that the proposed method can not only effectively deal with the sliding of rolling elements,but also analyze multiple natural frequency signals.By the two-step strategy,the weak fault features of rolling bearing that submerged in noise are extracted effectively.The performance of the presented method is verified using numerical simulation and practical rolling bearing data.