基于主元和判别集成分析的模拟电路故障诊断

Fault diagnosis of analog circuit by integrating principal component analysis with linear discriminant analysis

提出了主元和线性判别的集成分析算法以实施模拟故障数据的特征提取过程和方法.该集成分析方法首先对模拟故障数据进行主元分析,然后在主元变换空间实行线性判别分析,最后将所获得的最优判别特征模式应用于模式分类器进行故障诊断.仿真结果表明,所提出的方法能够充分利用线性方法的计算简便优势,增强单一主元分析或线性判别分析的特征提取性能,获取故障数据集的本质特征,简化模式分类器的结构,降低系统运行的计算成本.

The method of feature extraction on analog fault data by integrating principal component analysis（PCA） and linear discriminant analysis（LDA） is proposed. Firstly, PCA on analog fault data is carried out. Then, LDA is implemented in the transformed PCA space for obtaining the optimal discriminant features. Finally, the acquired patterns are applied to the pattern classifier for fault diagnosis. The simulation results show that the proposed method can take advantage of the simple computation of linear methods, enhance the performance of feature extraction of single PCA or LDA method, acquire the intrinsic features in fault data set, simplify the structure of the pattern classifier and reduce the computational cost of the diagnostic system.