基于信息图与MK-MOMEDA的齿轮箱复合故障诊断

Compound Fault Diagnosis of Gearbox Based on Infogram and MK-MOMEDA

针对齿轮箱复合故障振动信号传递路径复杂多变,早期微弱故障信号易受到背景噪声的严重干扰,使得传统方法对复合故障冲击特征难以准确分离的问题,提出一种信息图和多点峭度最优最小熵解卷积修正(MK-MOMEDA)的复合故障诊断方法。首先,利用平均谱负熵所得的信息图选择最佳的带宽和中心频率对复合故障信号进行带通滤波,降低噪声成分的影响;然后,计算滤波信号的多点峭度谱,识别谱图中包含的故障冲击周期成分,并设定适当的故障周期区间,进行MOMEDA运算,提取不同的故障特征;最后,通过1.5维能量谱进行特征增强,分析谱图中突出的故障特征频率,从而判别故障类型。实验平台模拟齿轮箱两种故障的复合情况,结果表明该方法能实现复合故障的准确分离。

In view of the complex and variable transmission path of the compound gearbox vibration signal, the early weak fault signal is susceptible to severe interference from background noise, which makes it difficult to accurately separate the impact characteristics of the compound gearbox by traditional methods. A infogram and multipoint kurtosis multipoint optimal minimum entropy deconvolution adjusted(MK-MOMEDA) composite fault diagnosis method is proposed. First, the infogram obtained by the average spectral negative entropy is used to select the optimal bandwidth and center frequency to perform band-pass filtering on the composite fault signal to reduce the influence of noise components. Then, the multipoint kurtosis spectrum of the filtered signal is calculated to identify the components of the fault impact cycle included in the spectrum. The appropriate fault cycle interval is set, and the MOMEDA operation is performed to extract different fault features. Finally,the 1.5-dimensional energy spectrum is used to enhance the features, and the fault feature frequency highlighted in the spectrum is analyzed to determine the fault type. The compound situation of two faults of the gearbox is simulated on the experimental platform, and the results show that this method can achieve the accurate separation of compound faults.