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一种基于改进VMD多尺度色散熵和TVD-CYCBD的故障特征提取方法

A Fault Feature Extraction Method Based on Improved VMD Multi-Scale Dispersion Entropy and TVD-CYCBD.

作者信息

Yang Jingzong, Zhou Chengjiang, Li Xuefeng, Pan Anning, Yang Tianqing

机构信息

School of Dig Data, Baoshan University, Baoshan 678000, China.

School of Information, Yunnan Normal University, Kunming 650500, China.

出版信息

Entropy (Basel). 2023 Feb 2;25(2):277. doi: 10.3390/e25020277.

DOI:10.3390/e25020277
PMID:36832644
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9955811/
Abstract

In modern industry, due to the poor working environment and the complex working conditions of mechanical equipment, the characteristics of the impact signals caused by faults are often submerged in strong background signals and noises. Therefore, it is difficult to effectivelyextract the fault features. In this paper, a fault feature extraction method based on improved VMD multi-scale dispersion entropy and TVD-CYCBD is proposed. First, the marine predator algorithm (MPA) is used to optimize the modal components and penalty factors in VMD. Second, the optimized VMD is used to model and decompose the fault signal, and then the optimal signal components are filtered according to the combined weight index criteria. Third, TVD is used to denoise the optimal signal components. Finally, CYCBD filters the de-noised signal and then envelope demodulation analysis is carried out. Through the simulation signal experiment and the actual fault signal experiment, the results verified that multiple frequency doubling peaks can be seen from the envelope spectrum, and there is little interference near the peak, which shows the good performance of the method.

摘要

在现代工业中，由于机械设备的工作环境恶劣且工作条件复杂，故障引起的冲击信号特征往往淹没在强背景信号和噪声中。因此，难以有效提取故障特征。本文提出了一种基于改进的变分模态分解（VMD）多尺度分散熵和全变差分解（TVD）-循环双谱峭度解调（CYCBD）的故障特征提取方法。首先，利用海洋捕食者算法（MPA）优化VMD中的模态分量和惩罚因子。其次，将优化后的VMD用于对故障信号进行建模和分解，然后根据组合权重指标准则对最优信号分量进行滤波。第三，使用TVD对最优信号分量进行去噪。最后，CYCBD对去噪后的信号进行滤波，然后进行包络解调分析。通过仿真信号实验和实际故障信号实验，结果验证了从包络谱中可以看到多个倍频峰，且峰值附近干扰较小，表明该方法具有良好的性能。

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