Rolling bearing fault diagnosis method in various working conditions based on feature transfer learning

Abstract

The present invention provides a rolling bearing fault diagnosis method in various working conditions based on feature transfer learning, and relates to the field of fault diagnosis. The objective ofthe invention is to solve the problem that a rolling bearing, especially to various working conditions, is low in accuracy of diagnosis. The method comprise the steps of: employing a VMD (VariationalMode Decomposition) to perform decomposition of vibration signals of a rolling bearing in each state to obtain a series of intrinsic mode functions, performing singular value decomposition of a matrixformed by the intrinsic mode functions to solve a singular value or a singular value entropy, combining time domain features and frequency domain features of the vibration signals to construct a multi-feature set; introducing a semisupervised transfer component analysis method to perform multinuclear construction of a kernel function thereof, sample features of different working conditions are commonly mapped to a shared reproducing kernel Hilbert space so as to improve the data intra-class compactness and the inter-class differentiation; and employing the maximum mean discrepancy embedding to select more efficient data as a source domain, inputting source domain feature samples into a SVM (Support Vector Machine) for training, and testing target domain feature samples after mapping. Therolling bearing fault diagnosis method in various working conditions has higher accuracy in the rolling bearing multi-state classification in various working conditions.

Description

technical field [0001] The invention relates to a rolling bearing fault diagnosis method under variable working conditions, and relates to the technical field of fault diagnosis. Background technique [0002] Rolling bearings are one of the key components of large-scale rotating machinery, and fault diagnosis is helpful to prevent equipment accidents [1] . When rolling bearings are actually working, the working conditions often change. In recent years, research on fault diagnosis of rolling bearings under variable working conditions has attracted widespread attention from scholars. [0003] Literature [2] combined Hilbert-Huang transform and Singular value decomposition (Singular value decomposition, SVD) to extract the characteristics of rolling bearing vibration signals, and then used recurrent neural network to realize bearing fault classification under variable working conditions; Literature [3] proposed a A method combining local mean decomposition and SVD can effect...

AI Technical Summary

Problems solved by technology

[0006] The invention aims at the problem that it is difficult or impossible to obtain a large amount of labeled vibration data for rolling bearings, especially under variable working conditions, resulting in low diagnosis accuracy, and further provides a rolling bearing fault diagnosis method under variable working conditions based on feature transfer learning

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