基于平方包络谱负熵准则的轴承早期复合故障特征提取方法

An early composite fault feature extraction method of bearing based on square envelope spectrum negentropy criterion

针对轴承早期复合故障诊断中故障特征难以提取的问题,提出基于平方包络谱负熵准则的优化群分解(optimized swarm decomposition,OSWD)方法。该方法首先构建基于平方包络谱负熵的优化准则,通过改进蝗虫优化算法(improved grasshopper optimization algorithm,IGOA)对群分解(swarm decomposition,SWD)算法中的阈值参数进行自适应寻优;然后,通过最优参数群分解实现复合故障振动信号的自适应分解,再对分解后的分量进行包络谱分析并提取复合故障特征频率,实现轴承早期复合故障诊断;最后,通过仿真分析和实际工程案例验证表明,该方法相比变分模态分解和群分解法,在有效提取早期复合故障特征方面效果更优。

In view of the problem that it is difficult to extract the early composite fault feature in fault diagnosis of bearings,an optimized Swarm decomposition(OSWD)method based on the criterion of square envelope spectrum negentropy was proposed.Firstly,an optimization criterion based on square envelope spectrum negentropy was constructed and the improved grasshopper optimization algorithm(IGOA)was used to optimize the threshold parameters of the Swarm decomposition(SWD)to obtain optimal threshold.Then the adaptive decomposition of the composite fault vibration signals was realized by using OSWD.The decomposed components were analyzed by using envelope spectrum analysis to extract the fault feature frequencies.The early composite fault diagnosis of bearings was accurately realized.Finally,the effectiveness of the proposed method was verified by the simulation analysis case and the fact engineering case.