基于MOMEDA和增强倒频谱的风电机组齿轮箱多故障诊断方法

Multi-fault diagnosis method for wind turbine gearbox based on MOMEDA and enhanced cepstrum

风电机组齿轮箱结构复杂,当齿轮、轴承存在多故障时,由于各故障强弱不同、故障间相互耦合及噪声干扰,造成故障诊断准确率低及漏诊问题。提出了一种基于多点最优最小熵解卷积(multipoint optimal minimum entropy deconvolution adjusted,MOMEDA)和增强倒频谱的风电机组齿轮箱多故障诊断方法。依据齿轮和轴承不同部位的故障特征频率设置合理的解卷积周期,利用MOMEDA对原始信号进行预处理;再通过增强倒频谱进一步抑制噪声干扰和增强故障特征;将增强倒频谱中的突出成分与齿轮箱故障特征频率对比,判断故障类型。实际风电机组齿轮箱多故障振动试验数据分析结果表明,该方法可以有效地提取出齿轮箱多故障特征信息。

The structure of wind turbine gearbox is complex.When there are many faults in gear and bearing,fault diagnosis accuracy is low and some faults’diagnosis is missed due to different fault intensities,mutual coupling between faults and noise interference.A multi-fault diagnosis method for wind turbine gearbox based on multi-point optimal minimum entropy deconvolution adjusted(MOMEDA)and enhanced cepstrum was proposed.Firstly,fault characteristic frequencies of different positions of gear and bearing were used to set reasonable deconvolution period,and the original signal was preprocessed by using MOMEDA.Then,enhanced cepstrum was used to further suppress noise interference and enhance fault features.Finally,prominent components in enhanced cepstrum were compared with fault characteristic frequencies of gearbox to determine the fault type.The analysis results of multi-fault vibration test data of actual wind turbine gearbox showed that the proposed method can effectively extract multi-fault feature information of gearbox.