分布式机电系统边缘节点信号预处理方法及应用设计

摘要：

为提高分布式机电系统边缘节点对本地数据过滤和分析的效率及准确性,提出了一种基于最优变分模式分解(Optimal variational mode decomposition, OVMD)的信号预处理方法。首先,利用一阶差分方法有效消除了原始信号中的奇异点,随之利用最优变分模态分解方法对信号进行模态分解,然后进行相关分析,确定各模式与原始信号的相关程度,从而从噪声信号中准确分离出真实的工作信号。在理论与仿真分析基础上,设计开发了分布式机电系统边缘节点预处理系统,并采用信噪比及均方根误差指标评价信号预处理效果。实验结果表明,该机电信号预处理方法及设计的边缘节点预处理系统能够提取不同特征的信号,提高重构信号信噪比,具有较高的保真性和可靠性,为后续系统健康监测、故障诊断等工作提供了数据保障。

Abstract： A signal pre-processing method based on optimal variational mode decomposition (OVMD) is proposed to improve the efficiency and accuracy of local data filtering and analysis of edge nodes in distributed electromechanical systems. Firstly, the singular points of original signals are eliminated effectively by using the first-order difference method. Then the OVMD method is applied for signal modal decomposition. Furthermore, correlation analysis is conducted to determine the degree of correlation between each mode and the original signal, so as to accurately separate the real operating signal from noise signal. On the basis of theoretical analysis and simulation, an edge node pre-processing system for distributed electromechanical system is designed. Finally, by virtue of the signal-to-noise ratio (SNR) and root-mean-square error (RMSE) indicators, the signal pre-processing effect is evaluated. The experimental results show that the OVMD-based edge node pre-processing system can extract signals with different characteristics and improve the SNR of reconstructed signals. Due to its high fidelity and reliability, this system can also provide data quality assurance for subsequent system health monitoring and fault diagnosis.