Abstract
Abstract
Feature extraction based on sparse representation is widely applied in the field of mechanical fault diagnosis. L1 norm regularization is a classical sparse regularization method, but this method has sparse underestimation for large-value features. A signal sparse representation method based on generalized multivariate logarithmic regularization is proposed in this paper. First, the sparse penalty term in the proposed method is designed according to the minimum convolution and logarithmic function, namely the generalized logarithmic non-convex penalty function. Then, the convexity condition of the objective function is studied to verify the feasibility of the method. The applicability of the method is also improved by analyzing the parameter constraint relation in the objective function. Finally, the sparse optimal solution is obtained by the forward-back splitting algorithm. Experiments show that compared with other non-convex sparse models, the proposed method can solve the problem of sparse underestimation more effectively and improve the reliability of gearbox fault diagnosis.
Funder
Cultivation Project for Basic Research and Innovation of Yanshan University
National Natural Science Foundation of China
Guides Local Science and Technology Development Foundation
Foreign Intellectual Project of Hebei Province
Subject
Applied Mathematics,Instrumentation,Engineering (miscellaneous)
Cited by
1 articles.
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