基于Wigner分布和经验模态分解的减速器故障诊断

为了降低全寿命费用和增强飞行安全性,对减速器故障进行检测和故障诊断是非常必要的。减速器振动信号的冲击性振动信号往往可以与故障引起的冲击联系在一起,因此冲击性信号可以作为一个故障的表征形式。为了刻画故障信号,许多研究探索在时频域内寻找微弱的故障特征信号。Wigner分布是最为常用的一种时频分布。然而,故障特征信号经常被其他部件的振动信号和Wigner分布双线性运算固有的交叉项所污染。为了减少这些干扰,本文将Wigner分布与经验模态分解结合起来。不同于传统的直接计算Wigner分布,将经验模态分解作为一个预处理环节。振动信号被分解为一系列固有模态函数。仅仅计算与啮合振动相关的固有模态函数的Wigner分布。这种方法应用于减速器试验台数据,结果表明诊断效果得到了明显提高。

Geabox Fault Diagnosis Based On Wigner Distribution and Empirical Mode Decomposition

To reduce life cycle cost and to improve flight safety,the detection and diagnosis of gearbox faults is of vital importance.Impulsive vibration signals in gearbox are often associated with impulse resulted from faults, and thus,impulsive signals can be used as the symptoms of faults.In order to characterize fault signals,many approaches were used to seek salient characteristic signals in time-frequency domain.Wigner distribution is a common time-frequency representation.However,the fault characteristic signals are always baffled by signals from other vibration sources and the inherent cross-terms in bilinear operation of the Wigner distribution.To minimize the effect of these disturbances,Wigner distribution was combined with empirical mode decomposition.Rather than calculating Wigner distribution directly,we present a method using empirical mode decomposition as a preprocessor.The vibration signal is decomposed into a sum of intrinsic mode functions.Only the Wigner distributions of the intrinsic mode functions which relate to the gear meshing vibration are calculated.This approach was applied to the diagnosis of a gearbox test data,the results show that effectiveness of diagnosing is significantly improved.