基于KPCA和PSOSVM的异步电机故障诊断

针对异步电机故障振动信号具有较强的非线性特征，而传统的线性分析方法易造成振动信号非线性成分的丢失这一情况，提出一种核主元分析和粒子群支持向量机相结合的异步电机故障诊断方法。利用核函数实现输入空间到高维特征空间的非线性映射以及对映射数据的主元分析，得到原始样本的非线性主元，实现特征提取和数据压缩，将获得的核主元特征通过支持向量机进行模式识别。采用距离比值法和粒子群算法分别对核主元分析和支持向量机的参数进行双重优化选择。实验结果表明，该方法能有效提取故障信号的非线性特征，具有较强的非线性模式识别能力，相比主元分析和支持向量机方法，分类效果更好，实时性更强，可快速有效实现异步电机故障诊断。

Fault Diagnosis of Asynchronous Motor Based on KPCA and PSOSVM

Asynchronous motor fault vibration signals have strong nonlinear characters that easily lose their nonlinearity with traditional linear methods, resulting in an impact fault diagnosis effect. Hence, a fault diagnosis method based on kernel principal component analysis (KPCA) and particle swarm optimization support vector machines (PSOSVM) is proposed. First, the kernel function is used to realize the nonlinear mapping from the original space to higher-dimensional space, and perform principal component analysis (PCA) on the mapping data. The nonlinear principal components of the original sample are then obtained; feature extraction and data compression are realized. The SVM uses the kernel principal features for pattern recognition. An optimizing method with a distance ratio and particle swarm algorithm is used for parameter optimization of the KPCA and SVM, respectively. The experimental results indicate that the method can effectively extract nonlinear features of a fault signal and perform well in nonlinear pattern recognition. Compared with the PCA and SVM methods, it has good classification effect and strong timeliness, both of which can quickly and effectively diagnose asynchronous motor faults.