Fault diagnosis model based on dimension reduction using linear local tangent space alignment

Based on dimension reduction using linear local tangent space alignment(LLTSA),a novel fault diagnosis model was proposed to achieve automatic,high-precise and general fault diagnosis of rotating machinery.With this model,mixed-domain feature sets of training and test samples were constructed to characterize the property of each kind of fault comprehensively by the fusion of empirical mode decomposition(EMD) and autoregression(AR) model coefficients.After that,LLTSA was introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.Finally,the low-dimensional eigenvectors of training and test samples were input into K-nearest neighbors classifier(KNNC) to carry out fault diagnosis.Comparing to the existing approaches,the proposed diagnosis model combines the advantages of mixed-domain features fusion in extensive extraction of fault feature,LLTSA in effective compression of fault information and KNNC in classification decision-making,and realizes the automation,high-precision and generality of fault diagnosis method.The diagnosis examples on different fault positions and severities of deep groove ball bearings validate the effectivity of proposed model.