基于局部均值分解与形态谱的旋转机械故障诊断方法

针对旋转机械不同类型故障会使振动信号具有不同形态特征及振动信号信噪比低等特点，提出基于局部均值分解（Local Mean Decomposition，LMD）与形态谱的旋转机械故障诊断方法。其中的LMD能对旋转机械原始振动信号进行降噪处理，而形态谱则能反映振动信号的形态特征，从而能判断旋转机械的工作状态。将该方法用于转子系统故障诊断，分析结果表明，该方法能有效提取旋转机械故障振动信号的故障特征，能准确识别旋转机械的故障状态。

A rotating machinery fault diagnosis method based on local mean decomposition and pattern spectrum

According to the characteristics including rotating machinery with different types of faults will make the vibration signals have different morphological features and the signal to noise ratio of vibration signals is low, a rotating machinery fault diagnosis method based on local mean decomposition (LMD) and pattern spectrum is proposed. In this method, the original rotating machinery vibration signals can be denoised by LMD, and the morphological features of vibration signals can be reflected by pattern spectrum, thus the working states of rotating machinery can be judged. This method is applied to the rotor system fault diagnosis, and the analytical results from the fault vibration signals of rotor system indicate that this method can extract the fault characteristics from rotating machinery vibration signals effectively and recognize the fault states of rotating machinery accurately.