基于AR-MOMEDA的车辆齿轮箱故障诊断方法

Vehicle gearbox fault diagnosis method based on AR-MOMEDA

在齿轮箱故障诊断过程中,多点最优最小熵反褶积(MOMEDA)能够连续提取周期脉冲,对信号的处理效率优于传统的最小熵反褶积(MED)。但MOMEDA在故障诊断中抗噪声能力差,故障频率容易淹没在噪声中,导致错误诊断。为此首先对振动信号进行预处理,将振动信号分为高频含噪分量以及低频剩余分量;然后提出一种AR-MOMEDA算法,对高频信号分量进行降噪。仿真实验以及工程应用结果表明,该方法比传统的MOMEDA更能够有效地提取故障特征,具有较强的抗噪能力。

In the process of gearbox fault diagnosis,the multi-point optimal minimum entropy deconvolution(MOMEDA)can continuously extract periodic pulses,and the signal processing efficiency is better than the traditional minimum entropy deconvolution(MED).But MOMEDA has poor anti-noise ability in fault diagnosis,and the fault frequency is easy to be submerged in noise,leading to misdiagnosis.Aiming at above problems,the vibration signal is preprocessed first,which divides the vibration signal into high-frequency noise components and low-frequency residual components;then an AR-MOMEDA algorithm is proposed to reduce the noise of high-frequency signal components.Simulation experiments and engineering application results show that this method can extract fault features more effectively than traditional MOMEDA and has stronger anti-noise ability.