自适应VMD及其在状态跟踪及故障检测中的应用

Adaptive VMD and its application in state tracking and fault detection

针对变分模态分解(variational modal decomposition,VMD)的特征提取性能受到参数影响的问题,以及故障状态跟踪的实时性较差的问题,提出一种状态预警线构造方法和自适应VMD方法并将其用于机械零件的故障检测。首先,提取机械零件全寿命振动信号的退化特征,基于2σ准则构造状态预警线来跟踪机械零件的退化状态并检测故障预警点。然后,引入能量熵和互信息构造适应度函数,通过蚱蜢优化算法(grasshopper optimization algorithm,GOA)构造自适应VMD模型来检测预警点附近机械零件的故障状态。结果表明,提出的状态预警线能更及时有效地检测出故障预警点,自适应VMD能更准确地检测出机械零件故障,具有良好的应用价值。

Aiming at the problem that the feature extraction performance of variational modal decomposition(VMD)is affected by its parameters and the poor real-time performance of fault state tracking,an early warning approach and adaptive VMD method are proposed and applied to mechanical part fault detection.Firstly,the degradation characteristics of the full-life vibration signal of mechanical parts are extracted,and then the state warning line is constructed based on the 2σcriterion.Through the early warning line,the degradation state of mechanical parts can be tracked and the fault early warning points can be detected.Then,the energy entropy and mutual information are introduced to construct the fitness function,and an adaptive VMD model is constructed by grasshopper optimization algorithm(GOA)to detect the fault state of mechanical parts near the early warning point.The results show that the proposed state early warning line can detect the fault early warning points timelier and more effectively,and the adaptive VMD can detect the faults of mechanical parts more accurately,which have good application value.