基于VMD和SVDD的滚动轴承早期微弱故障检测和性能退化评估研究

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振动与冲击 ›› 2019, Vol. 38 ›› Issue (22) : 224-230.

论文

王斐1，房立清2，赵玉龙2，齐子元2

作者信息 +

Rolling bearing early weak fault detection and performance degradation assessment based on VMD and SVDD

WANG Fei1, FANG Liqing2, ZHAO Yulong2, QI Ziyuan2

Author information +

文章历史 +

摘要

针对滚动轴承早期微弱故障检测及故障状态监测问题，提出了一种基于变模态分解（variational mode decomposition, VMD）分解和支持向量数据描述（support vector data description, SVDD）的滚动轴承性能退化评估模型。首先对振动信号进行VMD分解，选取对性能退化较为敏感的本征模态分量，提取其奇异值，并结合信号的时域特征指标，复杂度指标组成特征向量矩阵作为滚动轴承综合特征指标。其次，以正常状态下的综合特征指标作为训练样本完成SVDD评估模型的构建，利用滚动轴承全寿命试验数据进行评估模型的验证；最后实验结果表明，该评估模型可以准确检测到滚动轴承早期微弱故障阶段的发生，同时可以很好的揭示滚动轴承性能退化规律，其评估效果优于模糊均值聚类（Fuzzy Mean Clustering，FCM）方法。

Abstract

In order to detect the early weak faults and monitor the fault condition of rolling bearings, a performance degradation evaluation model based on variational mode decomposition (VMD) and support vector data description (SVDD) was proposed. The vibration signal was decomposed by VMD, and the intrinsic mode component which was sensitive to the performance degradation was selected to extract its singular value.Combining the singular value with the time domain feature and the complexity feature vector matrix, a comprehensive feature index of the rolling bearing was coustituted. A SVDD evaluation model was constructed taking the comprehensive characteristics of the normal state of the bearing, as the training sample and the rolling bearing’s whole life test data were used to verify the degradation assessment model. The experimental results show that the evaluation model can accurately detect the early stage weak failure of rolling bearings. At the same time, the performance degradation assessment of rolling bearings can be effectively achieved. The evaluation effect is superior to that of the fuzzy C-means clustering (FCM) method.

导出引用

王斐1，房立清2，赵玉龙2，齐子元2. 基于VMD和SVDD的滚动轴承早期微弱故障检测和性能退化评估研究[J]. 振动与冲击, 2019, 38(22): 224-230

WANG Fei1, FANG Liqing2, ZHAO Yulong2, QI Ziyuan2. Rolling bearing early weak fault detection and performance degradation assessment based on VMD and SVDD[J]. Journal of Vibration and Shock, 2019, 38(22): 224-230

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