基于VMD-SVDD的滚动轴承性能退化评估

Evaluation of rolling bearing performance degradation based on VMD-SVDD

摘要

针对滚动轴承性能退化初期趋势不明显和早期故障难以检测的问题，提出了一种基于新筛选指标的变分模态分解（variational modal decomposition，VMD）信号预处理与支持向量数据描述（support vector data description，SVDD）相结合的性能退化评估方法。首先对原始信号进行变分模态分解；其次在模态分量（intrinsic mode function，IMF）选择问题上提出了一个新的筛选指标P，该指标的计算公式由包络谱峭度和Wasserstein距离共同组成，选择P值大于阈值M的模态分量进行信号重构；最后提取重构信号的均方根值、波形因子、峰峰值3种特征构建表征轴承性能退化的特征向量，并以健康样本的退化特征向量作为输入建立SVDD性能退化评估模型，用全寿命样本特征向量进行验证。实验结果表明，此方法对早期故障更敏感，能够准确检测到早期故障。

Abstract

Aiming at the problems that the initial trend of rolling bearing performance degradation was not obvious and the early fault was difficult to detect， a performance degradation evaluation method based on the combination of variational modal decomposition （VMD） signal preprocessing and support vector data description （SVDD） were proposed. Firstly， the original signal was decomposed by variational mode. Secondly， a new screening index P was proposed for the selection of modal components （IMF）. The calculation formula of this index consists of kurtosis of envelope spectrum and Wasserstein distance， and the modal components with P value greater than the threshold M were selected for signal reconstruction. Finally， the root mean square value， waveform factor and peak-to-peak value of the reconstructed signal were extracted to construct a feature vector representing the degradation of bearing performance， and the SVDD performance degradation evaluation model was established with the degradation feature vector of healthy samples as input， which was verified by the full-life sample feature vector. The experimental results show that this method is more sensitive to early faults and can accurately detect early faults.

关键词

变分模态分解 / 包络谱峭度 / Wasserstein距离 / 支持向量 / 数据描述 / 性能退化

Key words

variational modal decomposition / kurtosis of envelope spectrum / Wasserstein distance / support vector / data description / performance degradation

基金资助

国家自然科学基金(62003223)