Bearing Fault Feature Extraction Method Based on GA-VMD and Center Frequency

Yuxing Li,Yingmin Yi,Xinru Jiang,Bingzhao Tang,Jian G Zhou

doi:10.1155/2022/2058258

Yuxing Li, Yingmin Yi + Show 3 more

Open Access

https://doi.org/10.1155/2022/2058258

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Abstract

To promote the effect of variational mode decomposition (VMD) and further enhance the recognition performances of bearing fault signals, genetic algorithm (GA) is applied to optimize the combination of VMD parameters in this paper, and GA-VMD algorithm is put forward to improve the decomposition accuracy of VMD. In addition, combined with the center frequency, a feature extraction method based on GA-VMD and center frequency is proposed to ameliorate the difficulty of bearing fault feature extraction. Firstly, the bearing signal is decomposed into a series of intrinsic mode components (IMFs) by GA-VMD. Then, the Center Frequency of IMFs is extracted, and the recognition rate is calculated by k-nearest neighbor (KNN) algorithm. Simulation signal experiments state clearly that, compared with manual parameter setting-VMD algorithm and parameter optimization VMD algorithm based on particle swarm optimization (PSO), the decomposition result of GA-VMD has a smaller root mean square error and higher decomposition accuracy, which verifies the effectiveness of GA-VMD. The experimental results demonstrate that, by comparison with the feature extraction method based on envelope entropy, the feature extraction method based on center frequency has better inter class separability and higher mean recognition rate (the highest recognition rate of single feature is 94.5%, and in the case of multiple features, the recognition rate reaches 100% when four features are extracted) and can realize the accurate identification of different bearing fault signals.

Highlights

As an essential part of various mechanical equipment, bearings have been widely used in civil, industrial, and military applications [1, 2]
Because the center frequency obtained by variational modal decomposition (VMD) does not need to be calculated like other features, a feature extraction method based on genetic algorithm (GA)-VMD and center frequency is proposed and applied to bearing fault diagnosis
In order to verify the effectiveness of GA-VMD algorithm, the VMD decomposition results based on particle swarm optimization algorithm are compared with the proposed method in this paper

Summary

Introduction

As an essential part of various mechanical equipment, bearings have been widely used in civil, industrial, and military applications [1, 2]. Only relying on the separability of the extracted features will be susceptible to interference from noise signals Another critical step of bearing signal fault diagnosis is the signal processing method. In order to solve these problems, Dragomiretskiy et al proposed the variational modal decomposition (VMD) method in 2014 [21], which can achieve adaptive decomposition of the target signal by iteratively searching for the optimal solution of the variational model, so as to determine the center frequency and bandwidth of each mode. Because the center frequency obtained by VMD does not need to be calculated like other features, a feature extraction method based on GA-VMD and center frequency is proposed and applied to bearing fault diagnosis.

Basic Principles

Simulation Signal Analysis

Bearing Signal Feature Extraction

Classification of Bearing Signals

Findings

Conclusion