基于组稀疏学习与AVOA-XGBoost的轴承故障分级诊断

张吉祥1, 张孟健2, 王德光1, 杨明1

振动与冲击 ›› 2024, Vol. 43 ›› Issue (18) : 96-105.

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (18) : 96-105.
论文

基于组稀疏学习与AVOA-XGBoost的轴承故障分级诊断

  • 张吉祥1,张孟健2,王德光1,杨明1
作者信息 +

Bearing fault classification diagnosis based on group-sparsity learning and AVOA-XGBoost

  • ZHANG Jixiang1,ZHANG Mengjian2,WANG Deguang1,YANG Ming1
Author information +
文章历史 +

摘要

针对工业设备中轴承振动信号在噪声环境下故障分级诊断准确率低的问题,提出一种基于组稀疏学习与非洲秃鹫优化算法优化极端梯度提升树(African vultures optimization algorithm-extreme gradient boosting, AVOA-XGBoost)的轴承故障分级诊断方法。首先,利用组稀疏学习对轴承振动信号进行重构,以降低噪声水平并更有效地表征故障脉冲。然后,对重构后的信号提取时域、频域和熵值特征并构建特征集。最后,利用AVOA自适应优化XGBoost超参数以建立稳健的XGBoost模型,进而高效实现轴承故障分级诊断。实验结果表明,经过组稀疏学习重构的信号具备更强故障特征表示能力,相较于传统机器学习模型,采用AVOA-XGBoost模型进行分类能够取得更高准确率,所提方法能够有效诊断轴承故障类型及故障程度。

Abstract

In response to the challenge of low accuracy of bearing fault classification under strong background noises in industrial equipment, a bearing fault classification method based on group-sparsity learning and African vultures optimization algorithm-extreme gradient boosting (AVOA-XGBoost) is proposed. First, the bearing vibration signals are reconstructed using group-sparsity representation, which reduces the noise level and characterizes fault impulses more effectively. Then, time-domain, frequency-domain, and entropy features are extracted from the reconstructed signals and the feature set is constructed. Finally, the super parameters of XGBoost are adaptively adjusted by AVOA, which establishes a robust XGBoost for efficient bearing fault classification diagnosis. Experimental results demonstrate that the signals reconstructed by group-sparsity learning exhibit stronger fault characteristic representation, AVOA-XGBoost achieves higher classification accuracy compared with traditional machine learning models, and the proposed method can effectively diagnose the types and degrees of bearing faults.

关键词

轴承故障诊断 / 组稀疏学习 / 特征提取 / 非洲秃鹫优化算法 / XGBoost

Key words

bearing fault diagnosis / group-sparsity learning / feature extraction / African vulture optimization algorithm / XGBoost

引用本文

导出引用
张吉祥1, 张孟健2, 王德光1, 杨明1. 基于组稀疏学习与AVOA-XGBoost的轴承故障分级诊断[J]. 振动与冲击, 2024, 43(18): 96-105
ZHANG Jixiang1, ZHANG Mengjian2, WANG Deguang1, YANG Ming1. Bearing fault classification diagnosis based on group-sparsity learning and AVOA-XGBoost[J]. Journal of Vibration and Shock, 2024, 43(18): 96-105

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