基于EMD奇异值熵和GASVM的转子系统故障诊断方法

提出一种基于经验模态分解(EMD)和奇异值熵的转子系统故障特征提取方法,克服了奇异值分解相空间重构参数难以选择的问题。然后将奇异值和奇异值熵作为故障特征输入到支持向量机(SVM)中,利用遗传算法(GA)对支持向量机进行参数优化,实现了故障的精确诊断。最后通过对转子不平衡、碰摩和不平衡-碰摩耦合3种故障的正确诊断,证明该方法的有效性。

Rotor System Fault Diagnosis Based on EMD Singular Value Entropy and GASVM

A fault diagnosis approach for rotor system based on empirical mode decomposition ( EMD ) and singular value entropy methods was proposed, which has the difficulty in selecting phase-space reconstruction parameter in the process of singular value decomposition solved and the singular value and singular value entro-py taken as the fault feature to input into the support vector machine ( SVM) as well as the genetic algorithm ( GA) used to optimize the SVM parameters so as to realize preliminary fault diagnosis. Diagnosing rotor im-balance, rubbing and imbalance-rubbing coupling proves effectiveness of the proposed method.