Fault diagnosis of rotating machinery via multi-structure fusion discriminative projection

Abstract

Abstract Rotating machinery is one of the important parts of industrial production equipment, and it is of great practical significance for fault diagnosis. Aiming at the problem of classification difficulty caused by feature interleaving in complex working conditions and high-dimension of rotating machinery fault samples, a rotating machinery fault diagnosis method based on multi-structure fusion discriminative projection (MFDP) is proposed. MFDP constructed intraclass and interclass hypergraph structures with multivariate relationships, fully revealing the higher-order association information among multiple samples. Besides, a tangential graph structure of MFDP is further constructed by combining the tangential affine of local samples to preserve the local tangential information of the manifold space. In the method, a unified objective optimization model of the discriminative hypergraph structures and local tangential graph structures is developed, and by solving the model, we can obtain fault structure features with well intraclass compactness and interclass separability. Extensive experiments on the Case Western Reserve University bearing dataset and Connecticut gear dataset show that the method has a good diagnostic accuracy of rotating machinery in different working conditions.