基于奇异值分解拓展应用的故障特征提取技术

Rolling Bearing Fault Feature Extraction Research Based on Application Development of Singular Value Decomposition

以奇异值分解理论为理论基础,通过对奇异值分解矩阵的架构分析,提出了滑移矩阵序列的架构方法。以该方法为指导,引入差异谱、主奇异和、最大特征值重构和最优化滤波器设计等方法,成功实现了滚动轴承故障特征提取。试验数据分析结果表明,提出的基于滑移矩阵序列奇异值分解的故障特征提取技术对于滚动轴承微弱冲击故障特征具有优越的识别和提取能力,对实现滚动轴承强噪声背景下的故障诊断具有重要意义。

We propose a slip vector construction method for fault diagnosis that is based on singular value decomposition theory and decomposition matrix frame analysis. Per this method's guidelines, we introduced the main singular value ratio, maximum eigenvalue reconstruction and optimized filter design methods. We successfully applied the proposed method to the fault feature extraction of rolling bearings. The experimental data analysis results showed that this method has suitably able to extract weak shock fault features. This paper has important implications in intelligent fault diagnosis of rolling bearings in circumstances of strong noise. © 2017, Editorial Department of JVMD. All right reserved.