中文摘要：

提出一种基于遗传算法分层优化多类最小二乘支持向量机（least squares supportveotor machine,LS-SVM）的故障诊断模型。首先将故障信号经验模态分解（empirical mode decomposition,EMD）为平稳本征模态（intrinsic mode function,I MF）分量,再选择表征故障调制特征的I MF分量并提取瞬时幅值能量作为故障特征输入到遗传算法分层优化好的采用多项式核的多类LS-SVM中进行故障识别。EMD分解可自适应分离故障调制信号;瞬时幅值能量矢量的不同表征各类故障的可分性;遗传算法分层优化惩罚因子和多项式核参数可以使LS-SVM摆脱对故障类型与模式编号映射关系先验知识的依赖,提高LS-SVM的故障预测精度和自适应诊断能力,并可以推广应用于线性、径向基、Sigmoid等核条件下的LS-SVM优化。一个深沟球轴承故障诊断实例说明该模型的有效性。

英文摘要：

A new fault diagnosis model is proposed based on Multi-Class Least Square Support Vector Machine optimized hierarchically by Genetic Algorithm（GA）. Original vibration signals are decomposed into several stationary IMFs. Then the instantaneous amplitude energy of the IMFs with fault modulation characteristics is computed and regarded as the input characteristic measure of the Poly-kernel Multi-Class LS-SVM for fault classification. EMD decomposition adaptively isolates the fault modulation signals from original signals. The differences among instantaneous amplitude energy vectors reflect the separability of different fault types. Adopting GA to optimize punish parameter and Poly-kernel parameters hierarchically can not only enhance fault prediction accuracy of Multi-Class LS-SVM with Poly-kernel,but also improve adaptive diagnosis capacity of LS-SVM. The GA-based hierarchical optimization is also applicable to Multi-Class LS-SVM with Lin-kernel,RBF-kernel or Sigmoid-kernel. The deep groove ball bearings fault diagnosis experiment shows the effectivity of this new model.