基于自适应高频谐波LMD法的风电机组故障诊断

Wind Turbine Fault Diagnosis Based on Adaptive High-frequency Harmonics LMD

针对实际应用中局部均值分解(LMD)法存在的模态混叠问题,提出了自适应高频谐波LMD法.分析了信号中异常事件对求取包络函数和均值函数的影响,将构造的自适应高频谐波加入到原始信号中,通过改变原始信号的极值点位置来抑制模态混叠现象.对含有典型异常事件的信号进行了自适应高频谐波LMD法和ELMD法仿真实验对比,验证了该算法的有效性和优越性.将该算法应用于风电机组传动系统故障诊断中,结果表明:采用该算法后,原有的模态混叠状况得到明显改善,并成功提取出轴系不平衡故障特征,可为风电机组故障诊断提供参考.

To solve the mode mixing problem of local mean decomposition(LMD)in actual applications,an adaptive high-frequency harmonics LMD was proposed.The effect of abnormal events on the envelope function and mean function was analyzed,and the adaptive high-frequency harmonics were constructed and added into the signal to deal with the mode mixing problem by changing the distribution of extreme points of the original signal.Simulation comparison was made to signals containing typical abnormal events between adaptive high-frequency harmonics LMD(AHLMD)and ensemble LMD(ELMD),illustrating the effectiveness and superiority of AHLMD,which was subsequently applied to fault diagnosis for the drive train system of a wind turbine.Results show that the mode mixing situation can be improved significantly and the shaft unbalance characteristics can be extracted successfully via the method,which therefore may serve as a reference for fault diagnosis of wind turbines.