组合模函数方法及其在机械故障诊断中的应用

Combined mode function and its application to mechanical fault diagnosis

针对信号被噪声污染时,经验模式分解(empirical mode decomposition,EMD)分析信号得到的本征模函数(intrinsic mode function,IMF)会发生明显畸变,从而降低经验模式分解精度这一问题,提出了组合模函数方法。该方法利用经验模式分解算法对信号进行分解,然后将特定的本征模函数组合起来,从而得到一个新的带宽依据信号特点自适应变化的带通滤波器,揭示信号特征。将所提出的方法应用于仿真数据及某电厂发电机组高压缸振动超限故障数据分析。结果表明:组合模函数方法能够较好地解决本征模函数畸变问题,明显提高经验模式分解精度,有助于精确提取故障特征和正确诊断故障类型;组合模函数方法对工程环境采集的机械设备故障数据分析和特征提取具有一定实用价值。

Empirical Mode Decomposition （EMD） decomposes a signal into a number of intrinsic mode functions （IMFs） based on the local characteristic time scales of the signal. The IMFs indicate the intrinsic oscillation modes embedded in the signal. An improved empirical mode decomposition, named combined mode function, is investigated to solve the problem that IMFs sometimes are distorted, and failed to represent the characteristics of the signal due to the effects of noises. EMD is applied to decompose a signal into some IMFs, then certain IMFs are combined to obtain a combined mode function, which is more accurate to present the features of the signal. By using combined mode function, one practically acquires a new adaptive band-pass filter bank. Simulation experiments demonstrate that the combined mode function can increase EMD precision when noises are introduced into a signal. Then, the proposed approach is used to analyze a practical fault signal of an electrical machine in a power plant in China. The results show that the combined mode function can extract the signal characteristics more accurately and is useful for diagnosing the mechanical fault correctly. 7 figs, 9 refs.