提出一种改进变分模态分解的轴承故障信号诊断方法。使用改进的奇异值分解降噪方法对信号进行降噪,然后对信号进行变分模态分解;利用分量信号的能量之和占原信号能量的比值,判断变分模态分解的分解效果,从而找出最佳分解层数;根据分量信号间的相关系数,判断中心频率相邻的分量信号是否来自信号中的同一调制部分;最后通过主要分量的包络谱找出故障特征频率,判断故障类型。通过对仿真信号和实际轴承故障信号进行处理,成功提取微弱频率特征信息,验证了该方法的有效性。
Abstract
A fault diagnosis method for bearing fault signal with improved variational mode decomposition was proposed. Firstly, the improved singular value decomposition denoising method was used to denoise the signal, and then the signal was subjected to variational mode decomposition.By using the ratio of the sum of the energy of the component signal to the original signal energy, the decomposition effect of the variational mode decomposition was judged to find the optimal decomposition layer. And based on the correlation coefficient between the component signals, it was judged whether the component signal adjacent to the center frequency was from the same modulation portion in the signal. Finally, the fault characteristic frequency was found by the envelope spectrum of the main component, and the fault type was judged.The weak frequency characteristic information was successfully extracted by processing the simulated signal and the actual bearing fault signal, and the effectiveness of the method was verified.
关键词
计量学 /
滚动轴承 /
故障诊断 /
变分模态分解 /
奇异值分解
Key words
metrology /
rolling bearing /
fault diagnosis /
variational mode decomposition /
singular value decomposition
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基金
国家自然科学基金(51575472, 61873226); 河北省自然科学基金(E2019203448)
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