基于EEMD分解的直驱式机电作动器故障诊断

基于集合经验模式分解 (EEMD,Ensemble Empirical Mode Decomposition)算法,给出一种机载直驱式双余度机电作动器(DDDR-EMA,Direct-Driven Dual-Redundancy Electro-Mechanical Actuator)复合故障诊断方法.EEMD对信号加入有限幅度的高斯白噪声,利用高斯白噪声频率均匀分布的统计特性使信号在不同尺度上保持连续性,解决了经验模式分解的模式混叠缺陷并保留了自适应性.将EEMD方法应用于机载DDDR-EMA故障诊断实验振动信号分析,先对实测信号进行分解,得到一组无模式混叠的固有模式函数;再采用不同的方法分析各频段,提取各频段包含的故障特征.实验结果表明:与经验模式分解相比EEMD能提高故障信号的分析精度,准确诊断机载DDDR-EMA的复合故障.

Fault diagnosis of direct-driven electromechanical actuator based on ensemble empirical mode decomposition

Based on the ensemble empirical mode decomposition(EEMD), an fault diagnosis method was proposed to diagnose complex faults of the direct-driven dual-redundancy electromechanical actuator (DDDR-EMA). By adding different finite amplitude Gaussian white noises to the signal, EEMD keep the signal continuous in different time span. EEMD well solved the problem of mode mixing involved in the empirical mode decomposition, as well as kept adaptation. The vibration signal from the fault diagnosis experiment of air-borne DDDR-EMA was decomposed by EEMD, a group of the intrinsic mode functions were obtained, and several methods were used to extract fault features from different signal frequency bands. The experimental result verified that EEMD can improve the analysis precision of fault signal, and can diagnose complex faults of airborne DDDR-EMA more accurately comparing with EMD.