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基于谐波辅助多变量经验模态分解和转移熵的旋转机械早期故障检测方法

Early Fault Detection Method for Rotating Machinery Based on Harmonic-Assisted Multivariate Empirical Mode Decomposition and Transfer Entropy.

作者信息

Wu Zhe, Zhang Qiang, Wang Lixin, Cheng Lifeng, Zhou Jingbo

机构信息

School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.

School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing 100081, China.

出版信息

Entropy (Basel). 2018 Nov 13;20(11):873. doi: 10.3390/e20110873.

DOI:10.3390/e20110873
PMID:33266597
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7512455/
Abstract

It is a difficult task to analyze the coupling characteristics of rotating machinery fault signals under the influence of complex and nonlinear interference signals. This difficulty is due to the strong noise background of rotating machinery fault feature extraction and weaknesses, such as modal mixing problems, in the existing Ensemble Empirical Mode Decomposition (EEMD) time-frequency analysis methods. To quantitatively study the nonlinear synchronous coupling characteristics and information transfer characteristics of rotating machinery fault signals between different frequency scales under the influence of complex and nonlinear interference signals, a new nonlinear signal processing method-the harmonic assisted multivariate empirical mode decomposition method (HA-MEMD)-is proposed in this paper. By adding additional high-frequency harmonic-assisted channels and reducing them, the decomposing precision of the Intrinsic Mode Function (IMF) can be effectively improved, and the phenomenon of mode aliasing can be mitigated. Analysis results of the simulated signals prove the effectiveness of this method. By combining HA-MEMD with the transfer entropy algorithm and introducing signal processing of the rotating machinery, a fault detection method of rotating machinery based on high-frequency harmonic-assisted multivariate empirical mode decomposition-transfer entropy (HA-MEMD-TE) was established. The main features of the mechanical transmission system were extracted by the high-frequency harmonic-assisted multivariate empirical mode decomposition method, and the signal, after noise reduction, was used for the transfer entropy calculation. The evaluation index of the rotating machinery state based on HA-MEMD-TE was established to quantitatively describe the degree of nonlinear coupling between signals to effectively evaluate and diagnose the operating state of the mechanical system. By adding noise to different signal-to-noise ratios, the fault detection ability of HA-MEMD-TE method in the background of strong noise is investigated, which proves that the method has strong reliability and robustness. In this paper, transfer entropy is applied to the fault diagnosis field of rotating machinery, which provides a new effective method for early fault diagnosis and performance degradation-state recognition of rotating machinery, and leads to relevant research conclusions.

摘要

分析复杂非线性干扰信号影响下旋转机械故障信号的耦合特性是一项艰巨的任务。这一困难源于旋转机械故障特征提取的强噪声背景以及现有总体经验模态分解（EEMD）时频分析方法中存在的诸如模态混叠问题等弱点。为了定量研究复杂非线性干扰信号影响下旋转机械故障信号在不同频率尺度之间的非线性同步耦合特性和信息传递特性，本文提出了一种新的非线性信号处理方法——谐波辅助多元经验模态分解方法（HA - MEMD）。通过添加额外的高频谐波辅助通道并对其进行缩减，可以有效提高本征模态函数（IMF）的分解精度，并减轻模态混叠现象。仿真信号的分析结果证明了该方法的有效性。通过将HA - MEMD与转移熵算法相结合并引入旋转机械的信号处理，建立了基于高频谐波辅助多元经验模态分解 - 转移熵（HA - MEMD - TE）的旋转机械故障检测方法。利用高频谐波辅助多元经验模态分解方法提取机械传动系统的主要特征，并将降噪后的信号用于转移熵计算。建立了基于HA - MEMD - TE的旋转机械状态评估指标，以定量描述信号之间的非线性耦合程度，从而有效评估和诊断机械系统的运行状态。通过向不同信噪比的信号中添加噪声，研究了HA - MEMD - TE方法在强噪声背景下的故障检测能力，证明该方法具有较强的可靠性和鲁棒性。本文将转移熵应用于旋转机械的故障诊断领域，为旋转机械的早期故障诊断和性能退化状态识别提供了一种新的有效方法，并得出了相关研究结论。

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