基于电子系统状态监测为研究背景,传统的Kernel Principal Component Analysis(核主成份分析法,简称KPCA)在状态监测过程中做数据特征降维处理,使得电路状态数据在消除冗余信息的同时,也能在相应的模型算法计算中很大程度的减少计算步骤,但是KPCA法的降维数据处理过程对数据样本贡献率的识别能力有不足之处,虽然达到了降维的目的,但是对特征样本数据的信息保留能力存在不足。本文中采用经验模态分解法(Empirical Mode Decomposition,简称 EMD)对输出信号进行采集处理作为样本数据,设计基于 Fisher准则的状态信息识别能力分析,采用 Estimation of Distribution Algorithms(种群算法,简称 EDA)对KPCA分析法进行改进研究,通过对数据处理,最大限度的保留状态主信息,使得在电路系统状态监测过程中减小实验误差,为后续故障预测打下基础。%Condition monitoring based on electronic system as the research background,the traditional Kernel Principal Component Analysis (Kernel Principal Component Analysis,KPCA)do in the process of condition monitoring data feature dimension reduction process,makes the circuit state data at the same time of eliminating redundant information,as well as the corresponding calculation model algorithm greatly reduces computation steps,but KPCA method of dimension reduction data processing for the contribution rate of the data sample inadequacies in the ability to recognize,though achieved the pur-pose of dimension reduction,but information on the characteristics of the sample data retention capability shortcomings.This article USES the method of Empirical Mode Decomposition (Empirical Mode Decomposition,the EMD)was carried out on the output signal as sample data collection and processing,design based on Fisher criterion of state information recognition a-bility analysis,the Estimation of Distribution Algorithms (population algorithm,referred to as EDA)to improve the KPCA analysis research,through the data processing,maximum retention state master information,make the circuit system de-crease experimental error in the process of condition monitoring,fault prediction to lay the foundation for the follow-up.
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