一种用于机床角度头故障诊断的双重降噪方法

角度头是数控机床必不可少的加工附件，由于长期处于恶劣的加工工况下，极易受到损坏。采集角度头的振动信号时，环境中大量的随机噪声会湮没故障特征信息，从而造成角度头故障特征提取困难。针对此问题，提出了一种基于总体平均经验模态分解（ensemble empirical mode decomposition，EEMD）及自相关的双重降噪方法。该方法采用自相关滤波方法对振动信号进行降噪预处理，再对降噪后的信号进行EEMD分解，随后采用遗传算法对EEMD输入参数优化，依据相关峭度系数准则筛选分解得到的固有模态函数（intrinsic mode function，IMF）分量进行信号重构。最后，对重构信号进行时频分析，提取角度头故障特征。对仿真和实测信号分析的结果表明，本文方法能够有效抑制噪声干扰，可准确提取到角度头的故障特征信息，为机床角度头的故障诊断提供依据。

A dual noise reduction method for angle head fault diagnosis

The angle head is an essential processing accessory for computer numerical control (CNC) machine tools. It is extremely vulnerable to damage under long-term harsh processing conditions. The strong random noise in the environment will annihilate the fault feature information of the angle head, which makes it difficult to extract data about fault features. To solve this problem, a dual noise reduction method based on ensemble empirical mode decomposition (EEMD) and autocorrelation is proposed. An autocorrelation filtering approach is used to preprocess the vibration signals data, and then the obtained signals are decomposed using EEMD. A genetic algorithm is then applied to optimize the input parameters of EEMD, and the intrinsic mode function (IMF) component obtained from the EEMD decomposition is selected to reconstruct the signal on the basis of a combination of kurtosis and correlation coefficients. The data for the angle head fault features can then be extracted from the reconstructed signals through time-frequency analysis. The predictions obtained using our method show good agreement with the measured data for the angle head. The results show that the proposed method can effectively suppress random noise and can accurately extract fault feature information for the angle head.