中文摘要：

提出一种基于稀化核函数主元分析的机械故障诊断新方法。该方法通过核函数映射将非线性问题转换成高维的线性特征空间，引入可变权值对高维空间中的映射数据的协方差矩阵进行稀化，应用似然估计得到优化权值，再作主元分析，提取其非线性特征，对机械故障模式进行识别。提出的方法继承核函数主元分析的优良性质，同时又能保证在识别效率不降低下的情况下有效提高故障识别速度。仿真和实验结果表明，稀化核函数主元分析和核函数主元分析方法都能得到很好的识别效果。然而，稀化核函数主元分析由于减少了核矩阵的计算量，因而模式识别速度大大加快。

英文摘要：

A new fault diagnosis method of machine based on the sparse kernel principle component analysis （5KPCA） is proposed. The proposed method can transform a nonlinear problem into the higher dimensional linear feature space by kernel function mapping. The covarianee matrix of the mapping data is sparse by the adjustable weights. The weights are optimized using a maximum-likelihood approach. Then the principle component analysis （PCA） method is used to this feature space to extract the nonlinear features. The sparse kernel principle component analysis （SKPCA） reserves the merit of kernel principle component analysis （KPCA）, at the same time the speed of pattern recognition is greatly enhanced under the recognized efficiency is not reduced. The simulation shows that SKPCA and KPCA almost have the same recognition effectiveness. However SKPCA reduces the computational overhead of the kernel matrix, so the recognition speed of SKPCA is greatly accelerated.