EMD趋势分析方法及其应用研究

趋势分析是一种重要的设备状态监测与故障诊断方法,对分析较长时间范围内设备运行状态的变化具有重要意义。研究了一种基于经验模式分解（Empirical Mode Decomposition,EMD）的设备运行状态趋势分析方法。研究表明,与传统方法（如最小二乘法、低通滤波法）相比,经验模式分解能够更准确地提取信号趋势信息。应用于某炼油厂透平烟机故障诊断,表明这种基于经验模式分解的趋势分析方法能够有效提取设备运行趋势信息,消除采样中随机因素的影响,为准确评估设备运行状态、诊断故障提供可靠依据,具有重要的现场实用价值。

Trend Analysis Approach Based on Empirical Mode Decomposition

An empirical mode decomposition(EMD)based approach for trend analysis of mechanical running condition is investigated. The results show that the approach can extract the trend of a signal more precisely, comparing with the traditional trend analysis methods, such as least square approximation and low-pass filtering. The presented approach has been applied to turbine fault diagnosis in an oil refinery, and the results indicate that it can obtain the trend of mechanical running condition effectively, then evaluate machine health condition and diagnose machine fault accurately.