Based on feature compression with orthogonal locality preserving projection(OLPP),a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition(EMD),and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine(MWSVM) and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis. Based on feature compression with orthogonal locality preserving projection (OLPP), a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery. Whith this model, the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition (EMD), and Shannon entropy is constructed to achieve high-dimensional eigenvectors. Order to replace the traditional feature extraction way which does the selection manually, OLPP is described to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination. After that, the low-dimensional eigenvectors of training samples are input into a Morlet wavelet support vector machine (MWSVM) and a trained MWSVM is obtained. Finally, the low- dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate ou r proposed model, the experiment of fault diagnosis of deep groove ball bearings is made, and the experiment results indicate that that recognition accuracy rate of the proposed diagnosis model for outer race crack, inner race crack and ball crack is more than 90% .Compared to the existing approaches, the proposed diagnosis model combines the strengths of EMD in fault feature extraction, OLPP in feature compression and MWSVM in pattern recognition, and realizes the automation and high-precision of fault diagnosis.