[1] Tong Qingjun, Hu Jianzhong, Jia Minping, Xu Feiyun, et al. Rolling bearing performance degradation evaluationby VMD and embedding selection-based NPE [J]. Journal of Southeast University (English Edition), 2019, 35 (4): 408-416. [doi:10.3969/j.issn.1003-7985.2019.04.002]

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Rolling bearing performance degradation evaluationby VMD and embedding selection-based NPE()

基于VMD和嵌入选择NPE的滚动轴承性能退化评估

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

35

Issue:

2019 4

Page:

408-416

Research Field:

Mechanical Engineering

Publishing date:

2019-12-30

Info

Title:

Rolling bearing performance degradation evaluationby VMD and embedding selection-based NPE

基于VMD和嵌入选择NPE的滚动轴承性能退化评估

Author(s):

Tong Qingjun, Hu Jianzhong, Jia Minping, Xu Feiyun

School of Mechanical Engineering, Southeast University, Nanjing 211189, China

童清俊, 胡建中, 贾民平, 许飞云

东南大学机械工程学院, 南京 211189

Keywords:

performance degradation evaluation;  variational mode decomposition(VMD);  neighborhood preserving embedding(NPE);  support vector data description(SVDD)

性能退化评估;  变分模态分解(VMD);  邻域保持嵌入(NPE);  支持向量数据描述(SVDD)

PACS:

TH17

DOI:

10.3969/j.issn.1003-7985.2019.04.002

Abstract:

In order to improve the incipient fault sensitivity and stability of degradation index in the rolling bearing performance degradation evaluation process, an embedding selection-based neighborhood preserving embedding(ESNPE)method is proposed. Firstly, the acquired vibration signals are decomposed by variational mode decomposition(VMD), and the singular value and relative energy of each intrinsic mode function(IMF)are extracted to form a high-dimensional feature set. Then, the NPE manifold learning method is used to extract the embedded features in the feature space. Considering the problem that useful embedding information is easily suppressed in NPE, an embedding selection strategy is built based on the Spearman correlation coefficient. The effectiveness of embeddings is measured by the coefficient absolute value, and useful embeddings are preserved in the early stage of bearing degradation by using the first-order difference method. Finally, the degradation index is established using the support vector data description(SVDD)model and bearing performance degradation evaluation is achieved. The proposed method was tested with the whole life experiment data of a rolling bearing, and the result was compared with the feature extraction methods of traditional principal component analysis(PCA)and NPE. The results show that the proposed method is superior in improving the incipient fault sensitivity and stability of the degradation index.

为了提高滚动轴承性能退化评估中退化指标的早期故障敏感性和稳定性, 提出了一种基于嵌入选择的邻域保持嵌入(ESNPE)方法.首先, 采用变分模态分解(VMD)对获得的振动信号进行分解, 提取各本征模态分量的奇异值和相对能量等组成高维故障特征集.然后, 采用NPE流行学习方法提取特征空间内的嵌入特征.针对传统NPE存在有效嵌入信息容易被抑制的问题, 构建了一种基于Spearman相关系数的嵌入选择策略.该策略通过相关系数的大小衡量嵌入特征的有效性, 并通过一阶差分的方法在轴承退化的早期阶段确定并保留有效嵌入特征.最后, 采用支持向量数据描述(SVDD)模型构建性能退化指标, 实现轴承性能退化评估.使用轴承全寿命退化实验数据, 并与传统的主成分分析(PCA)方法和NPE方法特征提取分析结果进行对比, 验证了所提方法在提升退化指标早期故障敏感性和稳定性方面具有优越性.

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Memo

Memo:

Biographies: Tong Qingjun(1994—), male, graduate; Hu Jianzhong(corresponding author), male, doctor, associate professor, hjz@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No. 51975117).
Citation: Tong Qingjun, Hu Jianzhong, Jia Minping, et al. Rolling bearing performance degradation evaluation by VMD and embedding selection-based NPE[J].Journal of Southeast University(English Edition), 2019, 35(4):408-416.DOI:10.3969/j.issn.1003-7985.2019.04.002.

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