Bearing composite fault diagnosis method based on improved local non-negative matrix factorization

Abstract

The invention discloses a bearing composite fault diagnosis method based on minimum correlation local non-negative matrix factorization. The method comprises the following steps: firstly, introducing a grasshopper optimization algorithm to optimize variational modal decomposition parameters, adaptively determining an optimal modal number and a penalty factor, inputting the optimal parameters into variational modal decomposition to decompose a vibration signal to obtain a plurality of modal components, and reconstructing the modal components to obtain an input matrix; secondly, introducing the minimum correlation constraint into a local non-negative matrix to form a minimum correlation local non-negative matrix factorization algorithm, reconstructing a plurality of modal components and an original signal into a modal matrix, and calculating the optimal dimension of the minimum correlation local non-negative matrix factorization; then performing optimal dimensionality decomposition on the input matrix by using a minimum correlation local non-negative matrix decomposition algorithm to obtain a basis matrix W and a coefficient matrix H, and finally performing envelope spectrum analysis on the basis matrix W to separate coupled bearing composite fault signals. The effectiveness of the method is verified through simulation analysis of the composite fault signal. The experimental data analysis result also shows that the method can effectively separate and diagnose the bearing composite fault.

Description

technical field [0001] The invention relates to a bearing compound fault diagnosis method, in particular to a bearing compound fault diagnosis method based on improved local non-negative matrix decomposition, and belongs to the technical field of fault diagnosis. Background technique [0002] Bearings are important parts in rotating machinery. According to relevant statistics: 30% of mechanical failures in rotating machinery using rolling bearings are caused by bearing failures, and in actual working conditions, a single failure of a bearing often induces failures in other positions A fault occurs, and then a state where multiple faults coexist. Therefore, it is of great significance to study the composite fault diagnosis method of rolling bearings. [0003] Blind source separation (BSS) is a powerful signal separation method developed rapidly in recent years, which can recover the source signal only from the observed signal when the source signal and the transmission chann...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a bearing compound fault diagnosis method based on improved local non-negative matrix decomposition, that is, minimum correlation local non-negative matrix decomposition, to solve the underdetermined problem in blind source separation and the optimal Determination of optimal parameters

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