应用奇异值分解和峭度分离滚动轴承复合故障

滚动轴承多故障特征影响故障诊断结果，为此提出一种结合奇异值分解和峭度的复合故障诊断方法。将采集的双通道多故障特征振动信号进行多层奇异值分解，利用奇异值差分谱和归一化峭度进行筛选和重构，实现对多故障特征的分别提取；通过滚动轴承内外圈故障实验，最终分离出轴承的2种故障。与直接采用原始信号诊断相比，该方法能够在背景噪声下准确分离频率相近的微弱故障成分，提高提取瞬态冲击信号特征的能力，能有效识别滚动轴承的故障类型和发生部位，提高复合故障诊断的准确性。实验结果表明，该方法可以有效地分离和提取滚动轴承多故障特征。

Multi-fault Feature Separation of Rolling Element Bearing Using Singular Value Decomposition and Kurtosis

In order to separate multi-fault of rolling element bearing, and improve diagnosis accuracy, a method based on singular value decomposition and kurtosis was proposed.The singular value decomposition was applied to decompose the picked two-channel vibration signals.Differential spectrum of singular value decomposition and normalized kurtosis were exploited to filter and restructure the components processed by singular value decomposition respectively.Then, hilbert envelope spectrum was utilized to extract single fault feature.Finally, Examples from experimental tests show that the developed approach is effective for bearing multi-fault detection.Compared with hilbert envelope spectrum method, this method can separate weak fault components in practice and improve the ability of extracting transient impact signals.Moreover it can recognize the rolling bearing fault types and locations effectively.The approach can be used for fault detection of failures arising from local damage of rolling element bearing.