基于增强型形态学滤波的风电机组轴承故障诊断方法

风电机组滚动轴承振动信号微弱故障特征易被背景噪声和其他频率干扰,提取难度较大。针对此类问题,提出一种增强型的形态学滤波及故障诊断方法。算法构造了一种新的形态学综合顶帽变换(morphological comprehensive filter-hat transform,MCFHT),将其用于强背景噪声下目标信号的故障脉冲提取,并通过非线性滤波器幅频响应考察其滤波性质,为振动检测中故障脉冲的提取提供理论依据;针对MCFHT变换滤波尺度选择问题,通过分析原振动信号自身振动特性,给出了一种自适应的尺度计算策略,有效提高了滤波处理的效率和性能;提出一种改进的包络导数能量算子用于增强形态学滤波后信号中故障冲击特征,并滤除带内噪声频率。仿真信号与风电机组轴承实际故障信号实验结果表明,该方法能有效提取随机噪声和谐波干扰下的故障特征信息,滤波效果强于传统方法,具有较好的工程应用价值。

A fault diagnosis method of wind turbine bearings based on an enhanced morphological filter

The weak fault-related features of vibration signal,which originates from wind turbine rolling element bearings,are generally immersed in environmental noise and harmonic interference and difficult to extract.This issue is addressed in this paper by proposing a new enhanced morphological filtering scheme for fault diagnosis.Firstly,a new morphology analysis method,named morphological comprehensive filter-hat transform(MCFHT),was constructed to extract fault-related impulses from measured signal in strong background noise.And its filtering property was investigated by the nonlinear filter frequency response characteristics,which provides a theoretical basis for the application of fault-related impulses extraction.Secondly,an adaptive scale selection strategy was explored to obtain appropriate filter scale for MCFHT.Thirdly,an improved envelope derivative energy operator was utilized to enhance the impulse characteristics of the signal after morphological filtering and to suppress the frequency of in-band noise.In the both simulation and experimental studies for wind turbine bearing,the proposed method delivered better fault feature extraction and noise reduction performance than the traditional methods.