Visual feature selection method based on non-negative matrix decomposition of fault binary classification

Abstract

A visual feature selection method based on non-negative matrix decomposition of fault binary classification is disclosed, firstly, the multi-classification problem is divided into a plurality of binary classification problems according to the permutation and combination mode, then the decomposition matrix is expressed by the non-negative matrix decomposition of the high-dimensional feature set, and finally, the effective classification features are selected by using the salient expression principle of the thermal map, and the sensitive features of the whole data set are extracted through the classification features; the invention can realize the mutual cooperation and complementary advantages of the computer and the human, and ensures the good classification performance of the low-dimensional feature subset while reducing the dimension of the original high-dimensional original feature.

Description

technical field [0001] The invention belongs to the technical field of mechanical equipment state detection and fault diagnosis, and in particular relates to a visual feature selection method based on fault binary classification non-negative matrix decomposition. Background technique [0002] As the complexity and level of integration of electromechanical systems continue to increase, so does the risk of equipment failure during operation. In order to accurately identify the faults initiated and evolved during the operation of the electromechanical system, and to diagnose and deal with abnormal components in a timely manner, condition monitoring and fault diagnosis become very necessary. With the continuous advancement of information acquisition technology, more and more feature quantities about system status and operating parameters can be obtained, including redundant and irrelevant feature information, which brings great challenges to subsequent diagnosis and identificati...

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Problems solved by technology

[0003] However, in the current eigenanalysis method based on non-negative matrix decomposition, the direct analysis of the basis matrix or coefficient matrix of the original multi-type fault sample matrix decomposition is used, and the algorithm is the center, and the selection process usually lacks human participation, resulting in the selection of The process is not transparent and intuitive enough, and the interpretability of the selection results is not strong, which limits the effect of feature analysis and selection

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