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For data-driven intelligent diagnosis, comprehensive mining of the information in the data is a key issue and a great challenge. The knowledge of how to discover potentially useful information in the data is particularly critical to raise the accuracy of fault pattern recognition. The structural and discriminative information in the data present a potential direction to solve the issue and overcome the challenge. In this study, a Similarity Balance Discriminant Projection (SBDP) algorithm is proposed, and by incorporating the optimized Support Vector Machine (SVM), and the develop a fault diagnosis model for rolling bearing fault diagnosis. To address the defects of unbalanced adjacency relations and non-strictly monotonic between-class weight functions, SBDP reconstructs new within-class adjacency graphs and between-class adjacency graphs by fusing structural information and category information, thus effectively preserving the intrinsic manifold structure information of high-dimensional feature data. The effectiveness and suitability of the proposed model is demonstrated by two kinds of bearing diagnosis applications. The application results indicate that SBDP is better able to extract features representing the intrinsic information of faults, the optimized SVM is effective in identifying the fault types with high accuracy.

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