改进双谱和经验模态分解在牵引电机轴承故障诊断中的应用

Fault Diagnosis of Traction Motor Bearings Using Modified Bispectrum and Empirical Mode Decomposition

铁路机车传动系统的故障诊断,对保障列车安全可靠运行、防范事故发生起重要的作用。为了有效诊断牵引电机轴承的早期故障,提出基于经验模态分解和改进双谱的故障特征提取方法。经验模态分解是一种数据驱动的信号处理算法,相当于一个自适应滤波器组,其可将信号分解成占据不同频带的固有模态函数,实现信号消噪。滚动轴承承载运转时,局部损伤点以故障特征频率反复撞击与之接触的其它元件表面,会引发机械系统共振;基于此,采用改进双谱分析轴承振动信号各分量间的相互作用,检测轴承故障特征频率。机车实际运行试验表明,所提方法能准确诊断牵引电机轴承的早期故障。

The diagnosis of mechanical faults in railway traction systems is of great important to both safety and reliability,which can avoid train crashes.A novel fault feature extraction approach based on empirica1 mode decomposition（EMD） and modified bispectrum is proposed to detect bearing incipient faults of locomotive traction motors in running condition.EMD is a fully data-driven signal processing method and acts essentially as a filter bank.The bearing vibration signal is adaptively decomposed into intrinsic oscillatory components called intrinsic mode functions（IMFs）,with each IMF corresponding to a specific range of frequency components contained within the signal.Bearing localized defects produce characteristic fault frequencies in the machine vibration and tend to modulate the machine＇s frequencies of mechanical resonance.The modified bispectrum is applied to identify these interactions and detect the bearing faults while it is still in an incipient stage.The proposed approach was effectively applied to the operation tests of SS7E type locomotive,and the bearing fault of traction motors was diagnosed successfully.