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当机械设备出现故障时,其振动信号中除了故障信号外,还混有能量较大的背景信号和噪声,表现出很强的非线性非平稳特征,利用传统的时域或频域方法很难有效的进行故障诊断。经验模态分解是一种自适应的信号处理方法,主要应用于非线性非平稳信号的分析。本文介绍了经验模态分解方法的原理及处理步骤,结合机械设备状态监测和故障诊断,论述了经验模态分解方法在滤波降噪、特征提取和状态识别中的应用情况。
When the mechanical equipment fails, the vibration signal in addition to the fault signal, but also mixed with a larger energy background signal and noise, showing a strong non-linear non-stationary features, the use of traditional time-domain or frequency domain method is difficult Effective troubleshooting. Empirical mode decomposition is an adaptive signal processing method, which is mainly applied to the analysis of nonlinear non-stationary signals. This paper introduces the principle and processing steps of the empirical mode decomposition method, and discusses the application of empirical mode decomposition in filtering noise reduction, feature extraction and state recognition combined with the state monitoring and fault diagnosis of mechanical equipment.