【摘要】 经验模式分解方法可以将非线性非平稳信号分解为有限的固有模式函数,在故障诊断中这个固有模式函数常常就是故障信号。但当两侧端点不为极值点时,会造成三次样条拟合的极值包络线大大偏离实际值,并且随着分解的不断进行向内“污染”。提出采用时间序列建模与预测方法,对原信号两端点进行预测,有效地消除了端点效应。指出经验模式分解具有分解的自适应性特点。最后,给出了齿轮箱振动信号的应用实例。更多还原

【Abstract】 The nonlinear and non-stationary data set can be decomposed into a finite and often small number of intrinsic mode functions’ by empirical mode decomposition(EMD). The intrinsic mode functions usually are the fault signal in fault diagnosis problem. The most serious problem of EMD method is the end effects due to the spline fitting at the data ends, i.e. the envelope curve fitted may have wide swings at the data ends if the ends are not the extremum. The decomposition quality would be polluted further alone with the decomposition. An improved empirical mode decomposition based on the time series analysis is derived. The envelope curve can be well fitted with the predicted extremum at the data ends from the time series model. It’s useful to eliminate the end effects. Compared with the wavelet analysis, EMD method is adaptive. An example from the gearbox signal is given to demonstrate the power of the proposed method.更多还原