IVMD融合奇异值差分谱的滚动轴承早期故障诊断

针对滚动轴承早期故障阶段存在特征信号微弱、故障识别相对困难的问题,提出了融合改进变分模态分解和奇异值差分谱的诊断方法。原始信号经改进变分模态分解方法处理后,被分解为若干本征模态函数分量,利用包络谱稀疏度指标筛选出最佳分量构造Hankel矩阵并进行奇异值分解,求取奇异值差分谱后,根据差分谱中的突变点重构信号,最终通过分析信号的包络谱可判断轴承的故障类型。利用改进变分模态分解融合奇异值差分谱的方法对轴承故障模拟及实测信号进行分析,均成功提取出微弱特征信息,能够实现滚动轴承早期故障的有效判别,具有一定的可靠性和应用价值。

An Incipient Fault Diagnosis Method for Rolling Bearing Based on Improved Variational Mode Decomposition and Singular Value Difference Spectrum

In the early fault period of rolling bearing, the characteristic signal is weak, and fault identification is very difficult. In order to solve this problem, a diagnostic method based on improved variational mode decomposition and singular value difference spectrum was proposed. The original signal was decomposed into several intrinsic mode function components after being processed by improving the variational mode decomposition method. Then, the best component was selected by the sparsity index of the envelope spectrum. The Hankel matrix was constructed through the best component, and singular value decomposition was operated. After acquiring the singular value difference spectrum, the signal was reconstructed through the maximum catastrophe point, and the fault type of bearing was judged by analyzing the envelope spectrum of the signal. The method was used to analyze simulated and measured signals of the fault bearing based on improved variational mode decomposition and singular value difference spectrum, and the weak characteristic information was extracted successfully. The results show that the proposed method can judge the incipient fault of rolling bearing effectively and have a certain reliability and application value.