机械振动信号内在模式的复杂性评估  

Empirical mode decomposition and complexity measurement of mechanical vibration signals

作　　者：王正英[1] 胥永刚[1] 李强[1] 王太勇[1] 

机构地区：[1]天津大学机械工程学院,天津300072

出　　处：《振动与冲击》2009年第1期81-83,共3页Journal of Vibration and Shock

基　　金：国家自然科学基金资助项目(编号:50475117);天津市科技发展计划项目(编号:06YFGZGX18200)

摘　　要：针对机械振动信号非平稳特性,利用经验模式分解法将其分解为若干个内在的振荡模式(即基本模式分量),从而使得不同的基本模式包含有不同的设备状态信息。借助近似熵的概念,可定理描述原始信号和各振荡模式的复杂性,实现对机械振动信号内在模式复杂性的定理评估。该方法不仅有助于揭示和认识转子系统的复杂动力学行为,还能有效地监测系统状态的早期变化,及时捕捉机组潜在的隐患,预防故障的升级恶化。工程应用实例表明,该方法可有效提取机组的故障信息,从而为机械设备状态监测和故障诊断提供一种行之有效的新方法。Aiming at nonstationary property of mechanical vibration signals,Empirical mode decomposition(EMD) can adaptively decompose signals to several intrinsic oscilation modes,and different mode contains different status information of an equipment.By means of approximate entropy(ApEn),the complexity of the original signal and its intrinsic oscillation modes can be quantified,thereby the intrinsic mode complexity of mechanical vibration signals can be quantitatively evaluated.One can use this method to understand the complex dynamic behavior of rotors, and monitor the change of running conditions of a mechanical equipment to prevent deterioration of fault.The engineering application results showed that this method can extract the fault information of a mechanical equipment,and provide an effective technology for condition monitoring and fault diagnosis of a mechanical equipment.

关 键 词：经验模式分解 近似熵 复杂性评估 状态监测 故障诊断 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]