基于改进VMD和Teager能量算子解调的轴箱轴承故障诊断方法

Fault Diagnosis Method of Axle Box Bearing Based on Improved VMD and Teager Energy Operator Demodulation

现行的高速列车轴箱轴承多采用温度监测和振动监测。针对高速列车轴箱轴承运行工况复杂、轴箱轴承振动信号故障特征难以提取的问题,提出改进VMD和Teager能量算子解调结合的故障诊断方法。该方法首先利用局域均值分解(LMD)的自适应分解性将轴承故障振动信号分解为多个PF分量,再通过构造融合冲击指标筛选有效的PF分量,有效分量被用于重构信号和确定VMD的模态参数K,最后选取VMD分解后信息熵最小值所在的IMF进行Teager能量算子解调分析,提取故障特征频。通过高速列车轴箱轴承专用试验台验证了该方法的有效性和优越性。结果表明,改进的VMD方法能有效克服垂向激励、环境噪声、共振等影响因素,提取出微弱的轴箱轴承早期故障特征。

Temperature monitoring and vibration monitoring are commonly used for current high-speed train axle box bearings.A fault diagnosis method based on improved VMD and Teager energy operator demodulation was proposed based on the complex operating conditions of high-speed train axle box bearings and the difficulty in extracting fault features of axle box bearing vibration signals,.We first decompose the bearing fault vibration signal into multiple PF components by using the adaptive decomposition of local mean decomposition(LMD).Then we filter out the effective PF components by constructing the fusion shock index.The effective components are used to reconstruct the signal and determine the VMD.Finally,we select the IMF where the minimum information entropy after VMD decomposition is located to carry out Teager energy operator demodulation analysis,and extract the fault characteristic frequency.The effectiveness and superiority of the method are verified by a special test bench for axle box bearings of high-speed trains.The results show that the improved VMD method can effectively overcome the vertical excitation,environmental noise,resonance and other influencing factors to extract weak early fault features of axle box bearings.